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Deshpande R, Augustine T. Smart transplants: emerging role of nanotechnology and big data in kidney and islet transplantation, a frontier in precision medicine. Front Immunol 2025; 16:1567685. [PMID: 40264762 PMCID: PMC12011751 DOI: 10.3389/fimmu.2025.1567685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2025] [Accepted: 03/25/2025] [Indexed: 04/24/2025] Open
Abstract
Kidney and islet transplantation has revolutionized the management of renal failure and diabetes. Transplantation is considered as excellent therapeutic intervention for most suitable patients. While advancements in the surgical aspects, immunosuppression and outcomes have potentially plateaued, new technologies have developed which could enhance transplantation with benefits to patients and clinical teams alike. The science of nanotechnology and big data advancements are two such technologies, collectively paving the way for smarter transplantation solutions. Nanotechnology offers novel strategies to overcome critical challenges, including organ preservation, ischemia-reperfusion injury and immune modulation. Innovations such as nanoparticle-based drug delivery systems, biocompatible encapsulation technologies for islet transplants, and implantable artificial kidneys are redefining the standards of care. Meanwhile, big data analytics harness vast datasets to optimize donor-recipient matching, refine predictive models for post-transplant outcomes, and personalize therapeutic regimens. Integrating these technologies forms a synergistic framework where nanotechnology enhances therapeutic precision and big data provides actionable insights, enabling clinicians to adopt proactive, patient-specific strategies. By addressing unmet needs and leveraging the combined potential of nanotechnology and big data, this transformative approach promises to improve graft survival, functionality, and overall patient outcomes, marking a paradigm shift in transplantation medicine. These developments will also be accelerated with integration of the rapidly advancing science of artificial intelligence.
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Affiliation(s)
- Rajkiran Deshpande
- Department of Renal and Pancreas Transplantation and General Surgery, Manchester Royal Infirmary, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Titus Augustine
- Department of Renal and Pancreas Transplantation and General Surgery, Manchester Royal Infirmary, Manchester University Foundation Trust, Manchester, United Kingdom
- Department Faculty of Biology, Medicine and Health, Division of Diabetes, Endocrinology and Gastroenterology, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
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2
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Rawashdeh B, Al-abdallat H, Arpali E, Thomas B, Dunn TB, Cooper M. Machine learning in solid organ transplantation: Charting the evolving landscape. World J Transplant 2025; 15:99642. [PMID: 40104197 PMCID: PMC11612896 DOI: 10.5500/wjt.v15.i1.99642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Revised: 10/17/2024] [Accepted: 11/06/2024] [Indexed: 11/26/2024] Open
Abstract
BACKGROUND Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes. AIM To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications. METHODS On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors. RESULTS Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus. CONCLUSION The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.
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Affiliation(s)
- Badi Rawashdeh
- Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI 53202, United States
| | | | - Emre Arpali
- Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI 53202, United States
| | - Beje Thomas
- Department of Nephrology, Medical College of Wisconsin, Milwaukee, WI 53226, United States
| | - Ty B Dunn
- Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI 53202, United States
| | - Matthew Cooper
- Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI 53202, United States
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van Delden C, Manuel O, Hirzel C, Walti LN, Khanna N, Hirsch HH, Dionyios N, Kohler P, Abela IA, Mueller NJ. The Swiss Transplant Cohort Study: Implications for Transplant Infectious Diseases Research. Transpl Infect Dis 2025; 27:e70023. [PMID: 40127403 DOI: 10.1111/tid.70023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2025] [Revised: 03/03/2025] [Accepted: 03/04/2025] [Indexed: 03/26/2025]
Abstract
The longitudinal, nationwide Swiss Transplant Cohort Study (STCS) follows > 92% of all transplant recipients with comprehensive data collection tailored to overall and organ-specific transplant outcomes. Transplant infectious disease events are assembled under the auspices of transplant ID specialists using common definitions. With over 6000 active patients and a median follow-up exceeding 6 years, the cohort offers a unique platform for understanding real-world epidemiology in transplanted patients. Beyond observational analysis, the STCS supports randomized controlled trials to address specific research questions. This overview highlights the achievements of the STCS and explores its future directions.
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Affiliation(s)
- Christian van Delden
- Transplant Infectious Diseases Unit, Service of Infectious Diseases, University Hospitals Geneva, University of Geneva, Geneva, Switzerland
| | - Oriol Manuel
- Infectious Diseases Service and Transplantation Center, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Cédric Hirzel
- Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laura N Walti
- Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Nina Khanna
- Division of Infectious Diseases, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Hans H Hirsch
- Department of Biomedicine Transplantation & Clinical Virology, University of Basel, Basel, Switzerland
| | - Neofytos Dionyios
- Transplant Infectious Diseases Unit, Service of Infectious Diseases, University Hospitals Geneva, University of Geneva, Geneva, Switzerland
| | - Philipp Kohler
- Division of Infectious Diseases, Infection Prevention and Travel Medicine, Cantonal Hospital of Sankt Gallen, St. Gallen, Switzerland
| | - Irene A Abela
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Nicolas J Mueller
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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4
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Zhang Z, Zhou X, Fang Y, Xiong Z, Zhang T. AI-driven 3D bioprinting for regenerative medicine: From bench to bedside. Bioact Mater 2025; 45:201-230. [PMID: 39651398 PMCID: PMC11625302 DOI: 10.1016/j.bioactmat.2024.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Revised: 11/01/2024] [Accepted: 11/16/2024] [Indexed: 12/11/2024] Open
Abstract
In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.
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Affiliation(s)
- Zhenrui Zhang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China
- “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China
| | - Xianhao Zhou
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China
- “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China
| | - Yongcong Fang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China
- “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China
- State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, PR China
| | - Zhuo Xiong
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China
- “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China
| | - Ting Zhang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, 100084, PR China
- “Biomanufacturing and Engineering Living Systems” Innovation International Talents Base (111 Base), Beijing, 100084, PR China
- State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, PR China
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Romero-Cristóbal M, Salcedo Plaza M, Bañares R. Why your doctor is not an algorithm: Exploring logical principles of different clinical inference methods using liver transplantation as a model. GASTROENTEROLOGIA Y HEPATOLOGIA 2025; 48:502215. [PMID: 38852780 DOI: 10.1016/j.gastrohep.2024.502215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/11/2024]
Abstract
The development of machine learning (ML) tools in many different medical settings is largely increasing. However, the use of the resulting algorithms in daily medical practice is still an unsolved challenge. We propose an epistemological approach (i.e., based on logical principles) to the application of computational tools in clinical practice. We rely on the classification of scientific inference into deductive, inductive, and abductive comparing the characteristics of ML tools with those derived from evidence-based medicine [EBM] and experience-based medicine, as paradigms of well-known methods for generation of knowledge. While we illustrate our arguments using liver transplantation as an example, this approach can be applied to other aspects of the specialty. Regarding EBM, it generates general knowledge that clinicians apply deductively, but the certainty of its conclusions is not guaranteed. In contrast, automatic algorithms primarily rely on inductive reasoning. Their design enables the integration of vast datasets and mitigates the emotional biases inherent in human induction. However, its poor capacity for abductive inference (a logical mechanism inherent to human clinical experience) constrains its performance in clinical settings characterized by uncertainty, where data are heterogeneous, results are highly influenced by context, or where prognostic factors can change rapidly.
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Affiliation(s)
- Mario Romero-Cristóbal
- Servicio de Aparato Digestivo, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, España; CIBEREHD, Instituto de Salud Carlos III, Madrid, España
| | - Magdalena Salcedo Plaza
- Servicio de Aparato Digestivo, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, España; CIBEREHD, Instituto de Salud Carlos III, Madrid, España; Facultad de Medicina, Universidad Complutense de Madrid, Madrid, España
| | - Rafael Bañares
- Servicio de Aparato Digestivo, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, España; CIBEREHD, Instituto de Salud Carlos III, Madrid, España; Facultad de Medicina, Universidad Complutense de Madrid, Madrid, España.
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Gong M, Jiang Y, Sun Y, Liao R, Liu Y, Yan Z, He A, Zhou M, Yang J, Wu Y, Wu Z, Huang Z, Wu H, Jiang L. Knowledge domain and frontier trends of artificial intelligence applied in solid organ transplantation: A visualization analysis. Int J Med Inform 2025; 195:105782. [PMID: 39761617 DOI: 10.1016/j.ijmedinf.2024.105782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2024] [Revised: 12/30/2024] [Accepted: 12/30/2024] [Indexed: 02/12/2025]
Abstract
BACKGROUND Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots, and key contributors in the field. METHODS 821 articles from the Web of Science Core Collection were exported for analysis. Microsoft Excel 2021 was used for descriptive statistics. VOSviewer, CiteSpace, Scimago Graphica, and Biblioshiny were used for bibliometric analysis. The ggalluvial package in R was utilized to create Sankey diagrams, and top articles were selected based on citation count. RESULTS This analysis reveals the rapid expansion of AI in SOT. Key areas include robotic surgery, organ allocation, outcome prediction, immunosuppression management, and precision medicine. Robotic surgery has improved transplant outcomes. AI algorithms optimize organ matching and enhance fairness. Machine learning models predict outcomes and guide treatment, while AI-based systems advance personalized immunosuppression. AI in precision medicine, including diagnostics and imaging, is crucial for transplant success. CONCLUSION This study highlights AI's transformative potential in SOT, with significant contributions from countries like the USA, Canada, and the UK. Key institutions such as the University of Toronto and the University of Pittsburgh have played vital roles. However, practical challenges like ethical issues, bias, and data integration remain. Fostering international and interdisciplinary collaborations is crucial for overcoming these challenges and accelerating AI's integration into clinical practice, ultimately improving patient outcomes.
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Affiliation(s)
- Miao Gong
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingsong Jiang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingshuo Sun
- Department of Obstetrics and Gynecology, Jinan Central Hospital of Shandong Province, Jinan, Shandong, China
| | - Rui Liao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yanyao Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zikang Yan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Aiting He
- Department of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Mingming Zhou
- Department of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Jie Yang
- Department of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Yongzhong Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhongjun Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - ZuoTian Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Hepatobiliary Pancreatic Tumor Center, Chongqing University Cancer Hospital, Chongqing, China.
| | - Hao Wu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Liqing Jiang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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7
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Pruinelli L, Balakrishnan K, Ma S, Li Z, Wall A, Lai JC, Schold JD, Pruett T, Simon G. Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review. BMC Med Inform Decis Mak 2025; 25:98. [PMID: 39994720 PMCID: PMC11852809 DOI: 10.1186/s12911-025-02890-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Accepted: 01/22/2025] [Indexed: 02/26/2025] Open
Abstract
BACKGROUND The principles of urgency, utility, and benefit are fundamental concepts guiding the ethical and practical decision-making process for organ allocation; however, LT allocation still follows an urgency model. AIM To identify and analyze data elements used in Machine Learning (ML) and Artificial Intelligence (AI) methods, data sources, and their focus on urgency, utility, or benefit in LT. METHODS A comprehensive search across Ovid Medline and Scopus was conducted for studies published from 2002 to June 2023. Inclusion criteria targeted quantitative studies using ML/AI for candidates, donors, or recipients. Two reviewers assessed eligibility and extracted data, following PRISMA guidelines. RESULTS A total of 20 papers were included, synthesizing results into five major categories. Eight studies were led by a Spanish team, focusing on donor-recipient matching and proposing machine learning models to predict post- LT survival. Other international studies addressed organ supply-demand issues and developed predictive models to optimize LT outcomes. The studies highlight the potential of ML/AI to enhance LT allocation and outcomes. Despite advancements, limitations included the lack of robust transplant-related benefit models and improvements in urgency models compared to MELD. DISCUSSION This review highlighted the potential of AI and ML to enhance liver transplant allocation and outcomes. Significant advancements were noted, but limitations such as the need for better urgency models and the absence of a transplant-related benefit model remain. Most studies emphasized utility, focusing on survival outcomes. Future research should address the interpretability and generalizability of these models to improve organ allocation and post-LT survival predictions.
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Affiliation(s)
- Lisiane Pruinelli
- Department of Family, Community and Health Systems Science, University of Florida, Gainesville, Florida, US.
- Department of Surgery, University of Florida, Gainesville, Florida, US.
| | - Kiruthika Balakrishnan
- Department of Family, Community and Health Systems Science, University of Florida, Gainesville, Florida, US
| | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Division of General Internal Medicine, University of Minnesota, Minneapolis, Minnesota, USA
| | - Zhigang Li
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Anji Wall
- Baylor University Medical Center in Dallas, Dallas, Texas, USA
| | - Jennifer C Lai
- Department of Medicine, University of California, San Francisco, California, USA
| | - Jesse D Schold
- Departments of Surgery and Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Timothy Pruett
- Department of Surgery, University of Minnesota, Minneapolis, Minnesota, US
| | - Gyorgy Simon
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
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Safi K, Pawlicka AJ, Pradhan B, Sobieraj J, Zhylko A, Struga M, Grąt M, Chrzanowska A. Perspectives and Tools in Liver Graft Assessment: A Transformative Era in Liver Transplantation. Biomedicines 2025; 13:494. [PMID: 40002907 PMCID: PMC11852418 DOI: 10.3390/biomedicines13020494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 02/07/2025] [Accepted: 02/10/2025] [Indexed: 02/27/2025] Open
Abstract
Liver transplantation is a critical and evolving field in modern medicine, offering life-saving treatment for patients with end-stage liver disease and other hepatic conditions. Despite its transformative potential, transplantation faces persistent challenges, including a global organ shortage, increasing liver disease prevalence, and significant waitlist mortality rates. Current donor evaluation practices often discard potentially viable livers, underscoring the need for refined graft assessment tools. This review explores advancements in graft evaluation and utilization aimed at expanding the donor pool and optimizing outcomes. Emerging technologies, such as imaging techniques, dynamic functional tests, and biomarkers, are increasingly critical for donor assessment, especially for marginal grafts. Machine learning and artificial intelligence, exemplified by tools like LiverColor, promise to revolutionize donor-recipient matching and liver viability predictions, while bioengineered liver grafts offer a future solution to the organ shortage. Advances in perfusion techniques are improving graft preservation and function, particularly for donation after circulatory death (DCD) grafts. While challenges remain-such as graft rejection, ischemia-reperfusion injury, and recurrence of liver disease-technological and procedural advancements are driving significant improvements in graft allocation, preservation, and post-transplant outcomes. This review highlights the transformative potential of integrating modern technologies and multidisciplinary approaches to expand the donor pool and improve equity and survival rates in liver transplantation.
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Affiliation(s)
- Kawthar Safi
- Department of Biochemistry, Medical University of Warsaw, 02-097 Warsaw, Poland; (K.S.)
| | | | - Bhaskar Pradhan
- Department of Biochemistry, Medical University of Warsaw, 02-097 Warsaw, Poland; (K.S.)
| | - Jan Sobieraj
- 1st Chair and Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Andriy Zhylko
- Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Banacha 1A, 02-097 Warsaw, Poland
| | - Marta Struga
- Department of Biochemistry, Medical University of Warsaw, 02-097 Warsaw, Poland; (K.S.)
| | - Michał Grąt
- Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Banacha 1A, 02-097 Warsaw, Poland
| | - Alicja Chrzanowska
- Department of Biochemistry, Medical University of Warsaw, 02-097 Warsaw, Poland; (K.S.)
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9
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Shourabizadeh H, Aleman DM, Rousseau LM, Zheng K, Bhat M. Classification-augmented survival estimation (CASE): A novel method for individualized long-term survival prediction with application to liver transplantation. PLoS One 2025; 20:e0315928. [PMID: 39823426 PMCID: PMC11741629 DOI: 10.1371/journal.pone.0315928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 12/03/2024] [Indexed: 01/19/2025] Open
Abstract
Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a classification task that ultimately yields survival curves, beginning with dataset augmentation to improve class imbalance for use with any classification model. Unlike other approaches, CASE additionally provides an exact survival time prediction. We demonstrate CASE on a liver transplant case study to predict >20 years survival post-transplant, finding that CASE dataset augmentation improved AUCs from 0.69 to 0.88 and F1 scores from 0.32 to 0.73. Compared to Kaplan-Meier, Cox, and RSF survival models, the CASE framework demonstrated better performance across various existing survival metrics, as well as our novel metric, mean of individual areas under the survival curve (mAUSC). Further, we develop novel temporal feature importance methods to understand how different features may vary in survival importance over time, potentially providing actionable insights in real-world survival problems.
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Affiliation(s)
- Hamed Shourabizadeh
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Dionne M. Aleman
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Louis-Martin Rousseau
- Department Mathematics & Industrial Engineering, Polytechnique Montréal, Montréal, QC, Canada
| | - Katina Zheng
- Division of Gastroenterology & Hepatology, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Division of Gastroenterology & Hepatology, University of Toronto, Toronto, ON, Canada
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10
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Calleja R, Rivera M, Guijo-Rubio D, Hessheimer AJ, de la Rosa G, Gastaca M, Otero A, Ramírez P, Boscà-Robledo A, Santoyo J, Marín Gómez LM, Villar Del Moral J, Fundora Y, Lladó L, Loinaz C, Jiménez-Garrido MC, Rodríguez-Laíz G, López-Baena JÁ, Charco R, Varo E, Rotellar F, Alonso A, Rodríguez-Sanjuan JC, Blanco G, Nuño J, Pacheco D, Coll E, Domínguez-Gil B, Fondevila C, Ayllón MD, Durán M, Ciria R, Gutiérrez PA, Gómez-Orellana A, Hervás-Martínez C, Briceño J. Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model. Transplantation 2025:00007890-990000000-00970. [PMID: 39780307 DOI: 10.1097/tp.0000000000005312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2025]
Abstract
BACKGROUND Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival. A risk stratification score integrated with the model of end-stage liver disease score in a donor-recipient (D-R) matching system was developed. METHODS This retrospective multicenter cohort study used 539 D-R pairs of cDCD livers recovered with NRP, including 20 donor, recipient, and NRP variables. The following machine learning-based classifiers were evaluated: logistic regression, ridge classifier, support vector classifier, multilayer perceptron, and random forest. The endpoints were the 3- and 12-mo graft survival rates. A 3- and 12-mo risk score was developed using the best model obtained. RESULTS Logistic regression yielded the best performance at 3 mo (area under the receiver operating characteristic curve = 0.82) and 12 mo (area under the receiver operating characteristic curve = 0.83). A D-R matching system was proposed on the basis of the current model of end-stage liver disease score and cDCD-NRP risk score. CONCLUSIONS The satisfactory performance of the proposed score within the study population suggests a significant potential to support liver allocation in cDCD-NRP grafts. External validation is challenging, but this methodology may be explored in other regions.
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Affiliation(s)
- Rafael Calleja
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
| | - Marcos Rivera
- Department of Computational Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain
| | - David Guijo-Rubio
- Department of Computational Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain
| | - Amelia J Hessheimer
- General and Digestive Surgery Department, Hospital Universitario La Paz, Madrid, Spain
| | | | - Mikel Gastaca
- Hepatobiliary Surgery and Liver Transplantation Unit, Biocruces Bizkaia Health Research Institute, Cruces University Hospital, University of the Basque Country, Bilbao, Spain
| | - Alejandra Otero
- General and Digestive Surgery Department, Complejo Hospitalario Universitario de A Coruña, A Coruña, Spain
| | - Pablo Ramírez
- General and Digestive Surgery Department, Hospital Clínico Universitario Virgen de la Arrixaca, IMIB, El Palmar, Spain
| | - Andrea Boscà-Robledo
- General and Digestive Surgery Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Julio Santoyo
- General and Digestive Surgery Department, Hospital Regional Universitario de Málaga, Spain
| | - Luis Miguel Marín Gómez
- General and Digestive Surgery Department, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | - Jesús Villar Del Moral
- General and Digestive Surgery Department, Hospital Universitario Virgen de las Nieves, Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Yiliam Fundora
- Division of Hepatobiliary and General Surgery, Department of Surgery, Institut de Malalties Digestives I Metabòliques (IMDiM), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Laura Lladó
- General and Digestive Surgery Department, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, Spain
| | - Carmelo Loinaz
- General and Digestive Surgery Department, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Manuel C Jiménez-Garrido
- General and Digestive Surgery Department, Hospital Universitario Puerta de Hierro, Majadahonda, Spain
| | - Gonzalo Rodríguez-Laíz
- General and Digestive Surgery Department, Hospital General Universitario de Alicante, Alicante, Spain
| | - José Á López-Baena
- General and Digestive Surgery Department, Hospital General Universitario Gregorio Marañón General University Hospital, Madrid, Spain
| | - Ramón Charco
- General and Digestive Surgery Department, Hospital Universitario Vall d'Hebron, Barcelona, Spain
| | - Evaristo Varo
- General and Digestive Surgery Department, Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain
| | - Fernando Rotellar
- General and Digestive Surgery Department, Hospital Universitario de Navarra, Pamplona, Spain
| | - Ayaya Alonso
- General and Digestive Surgery Department, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain
| | - Juan C Rodríguez-Sanjuan
- General and Digestive Surgery Department, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Gerardo Blanco
- General and Digestive Surgery Department, Hospital Universitario Infanta Cristina, Badajoz, Spain
| | - Javier Nuño
- General and Digestive Surgery Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - David Pacheco
- General and Digestive Surgery Department, Hospital Universitario Río Hortega, Valladolid, Spain
| | | | | | - Constantino Fondevila
- General and Digestive Surgery Department, Hospital Universitario La Paz, Madrid, Spain
| | - María Dolores Ayllón
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
| | - Manuel Durán
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
| | - Ruben Ciria
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
| | - Pedro A Gutiérrez
- Department of Computational Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain
| | - Antonio Gómez-Orellana
- Department of Computational Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain
| | - César Hervás-Martínez
- Department of Computational Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain
| | - Javier Briceño
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Córdoba, Spain
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11
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Malik S, Das R, Thongtan T, Thompson K, Dbouk N. AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease. J Clin Med 2024; 13:7833. [PMID: 39768756 PMCID: PMC11678868 DOI: 10.3390/jcm13247833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 12/13/2024] [Accepted: 12/19/2024] [Indexed: 01/11/2025] Open
Abstract
The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.
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Affiliation(s)
- Sheza Malik
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA;
| | - Rishi Das
- Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA; (R.D.); (T.T.)
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Thanita Thongtan
- Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA; (R.D.); (T.T.)
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Kathryn Thompson
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Nader Dbouk
- Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA; (R.D.); (T.T.)
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
- Emory Transplant Center, Emory University School of Medicine, Atlanta, GA 30322, USA
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12
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Wong HPN, Selvakumar SV, Loh PY, Liau JYJ, Liau MYQ, Shelat VG. Ethical frontiers in liver transplantation. World J Transplant 2024; 14:96687. [PMID: 39697458 PMCID: PMC11438941 DOI: 10.5500/wjt.v14.i4.96687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 08/26/2024] [Accepted: 09/10/2024] [Indexed: 09/20/2024] Open
Abstract
Liver transplantation represents a pivotal intervention in the management of end-stage liver disease, offering a lifeline to countless patients. Despite significant strides in surgical techniques and organ procurement, ethical dilemmas and debates continue to underscore this life-saving procedure. Navigating the ethical terrain surrounding this complex procedure is hence paramount. Dissecting the nuances of ethical principles of justice, autonomy and beneficence that underpin transplant protocols worldwide, we explore the modern challenges that plaques the world of liver transplantation. We investigate the ethical dimensions of organ transplantation, focusing on allocation, emerging technologies, and decision-making processes. PubMed, Scopus, Web of Science, Embase and Central were searched from database inception to February 29, 2024 using the following keywords: "liver transplant", "transplantation", "liver donation", "liver recipient", "organ donation" and "ethics". Information from relevant articles surrounding ethical discussions in the realm of liver transplantation, especially with regards to organ recipients and allocation, organ donation, transplant tourism, new age technologies and developments, were extracted. From the definition of death to the long term follow up of organ recipients, liver transplantation has many ethical quandaries. With new transplant techniques, societal acceptance and perceptions also play a pivotal role. Cultural, religious and regional factors including but not limited to beliefs, wealth and accessibility are extremely influential in public attitudes towards donation, xenotransplantation, stem cell research, and adopting artificial intelligence. Understanding and addressing these perspectives whilst upholding bioethical principles is essential to ensure just distribution and fair allocation of resources. Robust regulatory oversight for ethical sourcing of organs, ensuring good patient selection and transplant techniques, and high-quality long-term surveillance to mitigate risks is essential. Efforts to promote equitable access to transplantation as well as prioritizing patients with true needs are essential to address disparities. In conclusion, liver transplantation is often the beacon of hope for individuals suffering from end-stage liver disease and improves quality of life. The ethics related to transplantation are complex and multifaceted, considering not just the donor and the recipient, but also the society as a whole.
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Affiliation(s)
- Hoi Pong Nicholas Wong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Surya Varma Selvakumar
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Pei Yi Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Jovan Yi Jun Liau
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Matthias Yi Quan Liau
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Vishalkumar Girishchandra Shelat
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Surgical Science Training Centre, Tan Tock Seng Hospital, Singapore 308433, Singapore
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13
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Qiu S, Zhao Y, Hu J, Zhang Q, Wang L, Chen R, Cao Y, Liu F, Zhao C, Zhang L, Ren W, Xin S, Chen Y, Duan Z, Han T. Predicting the 28-day prognosis of acute-on-chronic liver failure patients based on machine learning. Dig Liver Dis 2024; 56:2095-2102. [PMID: 39004553 DOI: 10.1016/j.dld.2024.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND We aimed to establish a prognostic predictive model based on machine learning (ML) methods to predict the 28-day mortality of acute-on-chronic liver failure (ACLF) patients, and to evaluate treatment effectiveness. METHODS ACLF patients from six tertiary hospitals were included for analysis. Features for ML models' development were selected by LASSO regression. Models' performance was evaluated by area under the curve (AUC) and accuracy. Shapley additive explanation was used to explain the ML model. RESULTS Of the 736 included patients, 587 were assigned to a training set and 149 to an external validation set. Features selected included age, hepatic encephalopathy, total bilirubin, PTA, and creatinine. The eXtreme Gradient Boosting (XGB) model outperformed other ML models in the prognostic prediction of ACLF patients, with the highest AUC and accuracy. Delong's test demonstrated that the XGB model outperformed Child-Pugh score, MELD score, CLIF-SOFA, CLIF-C OF, and CLIF-C ACLF. Sequential assessments at baseline, day 3, day 7, and day 14 improved the predictive performance of the XGB-ML model and can help clinicians evaluate the effectiveness of medical treatment. CONCLUSIONS We established an XGB-ML model to predict the 28-day mortality of ACLF patients as well as to evaluate the treatment effectiveness.
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Affiliation(s)
- Shaotian Qiu
- The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China
| | - Yumeng Zhao
- The School of Medicine, Nankai University, Tianjin 300071, China
| | - Jiaxuan Hu
- The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China
| | - Qian Zhang
- Department of Gastroenterology and Hepatology, Tianjin Union Medical Center, Tianjin 300121, China; Tianjin Medical University, Tianjin 300070, China
| | - Lewei Wang
- Department of Gastroenterology and Hepatology, Tianjin Union Medical Center of Tianjin Medical University, Tianjin 300121, China
| | - Rui Chen
- Department of Gastroenterology and Hepatology, Tianjin Union Medical Center of Tianjin Medical University, Tianjin 300121, China
| | - Yingying Cao
- Department of Hepatology and Gastroenterology, The Third Central Clinical College of Tianjin Medical University, Tianjin 300170, China
| | - Fang Liu
- Department of Hepatology and Gastroenterology, The Third Central Clinical College of Tianjin Medical University, Tianjin 300170, China
| | - Caiyan Zhao
- Department of Infectious Disease, the Third Hospital of Hebei Medical University, Shijiazhuang 050051, China
| | - Liaoyun Zhang
- Department of Infection Disease, First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Wanhua Ren
- Infectious Department of Shandong First Medical University Affiliated Shandong Provincial Hospital, Jinan 250021, China
| | - Shaojie Xin
- Liver Failure Treatment and Research Center, The Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China
| | - Yu Chen
- Liver Disease Center (Difficult & Complicated Liver Diseases and Artificial Liver Center), Beijing You'an Hospital Affiliated to Capital Medical University, Beijing 100069, China
| | - Zhongping Duan
- Liver Disease Center (Difficult & Complicated Liver Diseases and Artificial Liver Center), Beijing You'an Hospital Affiliated to Capital Medical University, Beijing 100069, China
| | - Tao Han
- The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center, Tianjin 300121, China; Tianjin Medical University, Tianjin 300070, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center of Tianjin Medical University, Tianjin 300121, China; Department of Hepatology and Gastroenterology, The Third Central Clinical College of Tianjin Medical University, Tianjin 300170, China.
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14
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Liu X, Shen J, Yan H, Hu J, Liao G, Liu D, Zhou S, Zhang J, Liao J, Guo Z, Li Y, Yang S, Li S, Chen H, Guo Y, Li M, Fan L, Li L, Luo P, Zhao M, Liu Y. Posttransplant complications: molecular mechanisms and therapeutic interventions. MedComm (Beijing) 2024; 5:e669. [PMID: 39224537 PMCID: PMC11366828 DOI: 10.1002/mco2.669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 07/02/2024] [Accepted: 07/08/2024] [Indexed: 09/04/2024] Open
Abstract
Posttransplantation complications pose a major challenge to the long-term survival and quality of life of organ transplant recipients. These complications encompass immune-mediated complications, infectious complications, metabolic complications, and malignancies, with each type influenced by various risk factors and pathological mechanisms. The molecular mechanisms underlying posttransplantation complications involve a complex interplay of immunological, metabolic, and oncogenic processes, including innate and adaptive immune activation, immunosuppressant side effects, and viral reactivation. Here, we provide a comprehensive overview of the clinical features, risk factors, and molecular mechanisms of major posttransplantation complications. We systematically summarize the current understanding of the immunological basis of allograft rejection and graft-versus-host disease, the metabolic dysregulation associated with immunosuppressive agents, and the role of oncogenic viruses in posttransplantation malignancies. Furthermore, we discuss potential prevention and intervention strategies based on these mechanistic insights, highlighting the importance of optimizing immunosuppressive regimens, enhancing infection prophylaxis, and implementing targeted therapies. We also emphasize the need for future research to develop individualized complication control strategies under the guidance of precision medicine, ultimately improving the prognosis and quality of life of transplant recipients.
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Affiliation(s)
- Xiaoyou Liu
- Department of Organ transplantationThe First Affiliated Hospital, Guangzhou Medical UniversityGuangzhouChina
| | - Junyi Shen
- Department of OncologyZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Hongyan Yan
- Department of Organ transplantationThe First Affiliated Hospital, Guangzhou Medical UniversityGuangzhouChina
| | - Jianmin Hu
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Guorong Liao
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Ding Liu
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Song Zhou
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Jie Zhang
- Department of Organ transplantationThe First Affiliated Hospital, Guangzhou Medical UniversityGuangzhouChina
| | - Jun Liao
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Zefeng Guo
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Yuzhu Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Siqiang Yang
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Shichao Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Hua Chen
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Ying Guo
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Min Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Lipei Fan
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Liuyang Li
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Peng Luo
- Department of OncologyZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Ming Zhao
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
| | - Yongguang Liu
- Department of Organ transplantationZhujiang HospitalSouthern Medical UniversityGuangzhouChina
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15
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Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2024; 108:1700-1708. [PMID: 39042768 DOI: 10.1097/tp.0000000000004876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
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Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
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16
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Oyelade T, Moore KP, Mani AR. Physiological network approach to prognosis in cirrhosis: A shifting paradigm. Physiol Rep 2024; 12:e16133. [PMID: 38961593 PMCID: PMC11222171 DOI: 10.14814/phy2.16133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/12/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024] Open
Abstract
Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.
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Affiliation(s)
- Tope Oyelade
- Institute for Liver and Digestive Health, Division of MedicineUCLLondonUK
- Network Physiology Laboratory, Division of MedicineUCLLondonUK
| | - Kevin P. Moore
- Institute for Liver and Digestive Health, Division of MedicineUCLLondonUK
| | - Ali R. Mani
- Institute for Liver and Digestive Health, Division of MedicineUCLLondonUK
- Network Physiology Laboratory, Division of MedicineUCLLondonUK
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17
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Calleja R, Durán M, Ayllón MD, Ciria R, Briceño J. Machine learning in liver surgery: Benefits and pitfalls. World J Clin Cases 2024; 12:2134-2137. [PMID: 38680268 PMCID: PMC11045503 DOI: 10.12998/wjcc.v12.i12.2134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/08/2024] [Accepted: 03/29/2024] [Indexed: 04/16/2024] Open
Abstract
The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.
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Affiliation(s)
- Rafael Calleja
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - Manuel Durán
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - María Dolores Ayllón
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - Ruben Ciria
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
| | - Javier Briceño
- Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
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18
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Chongo G, Soldera J. Use of machine learning models for the prognostication of liver transplantation: A systematic review. World J Transplant 2024; 14:88891. [PMID: 38576762 PMCID: PMC10989468 DOI: 10.5500/wjt.v14.i1.88891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 03/15/2024] Open
Abstract
BACKGROUND Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. AIM To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. RESULTS Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.
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Affiliation(s)
- Gidion Chongo
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
| | - Jonathan Soldera
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
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19
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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20
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Ahn JC, Shah VH. Artificial intelligence in gastroenterology and hepatology. ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE 2024:443-464. [DOI: 10.1016/b978-0-443-15688-5.00016-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Drezga-Kleiminger M, Demaree-Cotton J, Koplin J, Savulescu J, Wilkinson D. Should AI allocate livers for transplant? Public attitudes and ethical considerations. BMC Med Ethics 2023; 24:102. [PMID: 38012660 PMCID: PMC10683249 DOI: 10.1186/s12910-023-00983-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented. METHODS We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic. FINDINGS Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender. CONCLUSIONS There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.
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Affiliation(s)
- Max Drezga-Kleiminger
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
| | - Joanna Demaree-Cotton
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
| | - Julian Koplin
- Monash Bioethics Centre, Monash University, Melbourne, Australia
| | - Julian Savulescu
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK
- Murdoch Children's Research Institute, Melbourne, Australia
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dominic Wilkinson
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
- Oxford Uehiro Centre for Practical Ethics, Faculty of Philosophy, University of Oxford, Oxford, OX1 2JD, UK.
- Murdoch Children's Research Institute, Melbourne, Australia.
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- John Radcliffe Hospital, Oxford, UK.
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22
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Strauss AT, Sidoti CN, Sung HC, Jain VS, Lehmann H, Purnell TS, Jackson JW, Malinsky D, Hamilton JP, Garonzik-Wang J, Gray SH, Levan ML, Hinson JS, Gurses AP, Gurakar A, Segev DL, Levin S. Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study. Hepatol Commun 2023; 7:e0239. [PMID: 37695082 PMCID: PMC10497243 DOI: 10.1097/hc9.0000000000000239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 06/28/2023] [Indexed: 09/12/2023] Open
Abstract
BACKGROUND The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions. METHODS In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. RESULTS Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS. CONCLUSIONS Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.
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Affiliation(s)
- Alexandra T. Strauss
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Carolyn N. Sidoti
- Department of Surgery, New York University, Grossman School of Medicine, New York, New York, USA
| | - Hannah C. Sung
- Department of Surgery, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Vedant S. Jain
- Department of Surgery, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Harold Lehmann
- Department of Medicine, Division of Biomedical Informatics & Data Science, School of Medicine, Baltimore, Maryland, USA
| | - Tanjala S. Purnell
- Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - John W. Jackson
- Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Daniel Malinsky
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA
| | - James P. Hamilton
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Jacqueline Garonzik-Wang
- Department of Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, Wisconsin
| | - Stephen H. Gray
- Department of Surgery, University of Maryland, School of Medicine, Baltimore, Maryland, USA
| | - Macey L. Levan
- Department of Surgery, New York University, Grossman School of Medicine, New York, New York, USA
| | - Jeremiah S. Hinson
- Department of Emergency Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Ayse P. Gurses
- Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ahmet Gurakar
- Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
| | - Dorry L. Segev
- Department of Surgery, New York University, Grossman School of Medicine, New York, New York, USA
| | - Scott Levin
- Department of Emergency Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland, USA
- Beckman Coulter, Brea, California, USA
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23
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Pontes Balanza B, Castillo Tuñón JM, Mateos García D, Padillo Ruiz J, Riquelme Santos JC, Álamo Martinez JM, Bernal Bellido C, Suarez Artacho G, Cepeda Franco C, Gómez Bravo MA, Marín Gómez LM. Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons. Front Surg 2023; 10:1048451. [PMID: 37808255 PMCID: PMC10559881 DOI: 10.3389/fsurg.2023.1048451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 07/18/2023] [Indexed: 10/10/2023] Open
Abstract
Background The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it. Material and method Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated "in situ" for transplantation, and those discarded after the "in situ" evaluation were considered as no transplantable liver grafts, while those grafts transplanted after "in situ" evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed. Results A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85. Conclusion The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.
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Affiliation(s)
| | | | | | - Javier Padillo Ruiz
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
| | | | - José M. Álamo Martinez
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
| | - Carmen Bernal Bellido
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
| | - Gonzalo Suarez Artacho
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
| | - Carmen Cepeda Franco
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
| | - Miguel A. Gómez Bravo
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
| | - Luis M. Marín Gómez
- HPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,Spain
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24
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Bambha K, Kim NJ, Sturdevant M, Perkins JD, Kling C, Bakthavatsalam R, Healey P, Dick A, Reyes JD, Biggins SW. Maximizing utility of nondirected living liver donor grafts using machine learning. Front Immunol 2023; 14:1194338. [PMID: 37457719 PMCID: PMC10344453 DOI: 10.3389/fimmu.2023.1194338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Objective There is an unmet need for optimizing hepatic allograft allocation from nondirected living liver donors (ND-LLD). Materials and method Using OPTN living donor liver transplant (LDLT) data (1/1/2000-12/31/2019), we identified 6328 LDLTs (4621 right, 644 left, 1063 left-lateral grafts). Random forest survival models were constructed to predict 10-year graft survival for each of the 3 graft types. Results Donor-to-recipient body surface area ratio was an important predictor in all 3 models. Other predictors in all 3 models were: malignant diagnosis, medical location at LDLT (inpatient/ICU), and moderate ascites. Biliary atresia was important in left and left-lateral graft models. Re-transplant was important in right graft models. C-index for 10-year graft survival predictions for the 3 models were: 0.70 (left-lateral); 0.63 (left); 0.61 (right). Similar C-indices were found for 1-, 3-, and 5-year graft survivals. Comparison of model predictions to actual 10-year graft survivals demonstrated that the predicted upper quartile survival group in each model had significantly better actual 10-year graft survival compared to the lower quartiles (p<0.005). Conclusion When applied in clinical context, our models assist with the identification and stratification of potential recipients for hepatic grafts from ND-LLD based on predicted graft survivals, while accounting for complex donor-recipient interactions. These analyses highlight the unmet need for granular data collection and machine learning modeling to identify potential recipients who have the best predicted transplant outcomes with ND-LLD grafts.
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Affiliation(s)
- Kiran Bambha
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Washington, Seattle, WA, United States
- Center for Liver Investigation Fostering discovery (C-LIFE), University of Washington, Seattle, WA, United States
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
| | - Nicole J. Kim
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Washington, Seattle, WA, United States
- Center for Liver Investigation Fostering discovery (C-LIFE), University of Washington, Seattle, WA, United States
| | - Mark Sturdevant
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - James D. Perkins
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Catherine Kling
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Ramasamy Bakthavatsalam
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
| | - Patrick Healey
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Pediatric Transplant Surgery Division, Department of Surgery, Seattle Children’s Hospital, Seattle, WA, United States
| | - Andre Dick
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Pediatric Transplant Surgery Division, Department of Surgery, Seattle Children’s Hospital, Seattle, WA, United States
| | - Jorge D. Reyes
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
- Division of Transplant Surgery, Department of Surgery, University of Washington, Seattle, WA, United States
- Pediatric Transplant Surgery Division, Department of Surgery, Seattle Children’s Hospital, Seattle, WA, United States
| | - Scott W. Biggins
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Washington, Seattle, WA, United States
- Center for Liver Investigation Fostering discovery (C-LIFE), University of Washington, Seattle, WA, United States
- Clinical and Bio-Analytics Transplant Laboratory (C-BATL), University of Washington, Seattle, WA, United States
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25
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Pomohaci MD, Grasu MC, Dumitru RL, Toma M, Lupescu IG. Liver Transplant in Patients with Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13091663. [PMID: 37175054 PMCID: PMC10178485 DOI: 10.3390/diagnostics13091663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. However, due to organ shortages, this treatment can only be applied to carefully selected patients according to clinical guidelines. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging, like radiology. With the help of these technologies, laborious tasks can be automated, and new lesion imaging criteria can be developed based on pixel-level analysis. Our objectives are to review the developing AI applications that could be implemented to better stratify liver transplant candidates. The papers analysed applied AI for liver segmentation, evaluation of steatosis, sarcopenia assessment, lesion detection, segmentation, and characterization. A liver transplant is an optimal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease. Furthermore, AI could provide solutions for improving the management of liver transplant candidates to improve survival.
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Affiliation(s)
- Mihai Dan Pomohaci
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mugur Cristian Grasu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Radu Lucian Dumitru
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Mihai Toma
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
| | - Ioana Gabriela Lupescu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania
- Department of Radiology, The University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania
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26
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Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology. Diagnostics (Basel) 2023; 13:diagnostics13040662. [PMID: 36832150 PMCID: PMC9955763 DOI: 10.3390/diagnostics13040662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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28
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Kalapala R, Rughwani H, Reddy DN. Artificial Intelligence in Hepatology- Ready for the Primetime. J Clin Exp Hepatol 2023; 13:149-161. [PMID: 36647407 PMCID: PMC9840075 DOI: 10.1016/j.jceh.2022.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/23/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.
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Key Words
- ACLF, acute on chronic liver failure
- AI, artificial intelligence
- ALD, alcoholic liver disease
- ALT, alanine transaminase
- ANN, artificial neural network
- AST, aspartate aminotransferase
- AUD, alcohol use disorder
- CHB, chronic hepatitis B
- CHC, chronic hepatitis C
- CLD, chronic liver disease
- CNN, convolutional neural network
- DL, deep learning
- FIB-4, fibrosis-4 score
- GGTP, gamma glutamyl transferase
- HCC, hepatocellular carcinoma
- HDL, high density lipoprotein
- ML, machine learning
- MLR, multi-nomial logistic regressions
- NAFLD
- NAFLD, non-alcoholic fatty liver disease
- NASH, non-alcoholic steatohepatitis
- NLP, natural language processing
- RF, random forest
- RTE, real-time tissue elastography
- SOLs, space-occupying lesions
- SVM, support vector machine
- artificial intelligence
- deep learning
- hepatology
- machine learning
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Affiliation(s)
- Rakesh Kalapala
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
| | - Hardik Rughwani
- Department of Gastroenterology, Asian Institute of Gastroenterology and AIG Hospitals, Hyderabad, India
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29
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Ivanics T, So D, Claasen MPAW, Wallace D, Patel MS, Gravely A, Choi WJ, Shwaartz C, Walker K, Erdman L, Sapisochin G. Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries. Am J Transplant 2023; 23:64-71. [PMID: 36695623 DOI: 10.1016/j.ajt.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/04/2022] [Accepted: 10/14/2022] [Indexed: 01/13/2023]
Abstract
Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models; however, potential performance and transferability of these models remain unclear. We used data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90-day post-LT mortality within and across countries. Predictive performance and external validity of each model were assessed. Prospectively collected data of adult patients (aged ≥18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Health Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United States) were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined (variables in common between the registries [harmonized]). The model performance was evaluated using area under the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, n = 1214; the United Kingdom, n = 5287; and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74; range, 0.73-0.74; harmonized: AUROC, 0.68; range, 0.50-0.73) and US (individualized ridge: AUROC, 0.71; range, 0.70-0.71; harmonized: AUROC, 0.66; range, 0.66-0.66) data sets. External model performance across countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing decision making in future LTs.
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Affiliation(s)
- Tommy Ivanics
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada; Department of Surgery, Henry Ford Hospital, Detroit, Michigan, USA; Department of Surgical Sciences, Akademiska Sjukhuset, Uppsala University, Uppsala, Sweden
| | - Delvin So
- The Centre of Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Marco P A W Claasen
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada; Department of Surgery, division of HPB & Transplant Surgery, Erasmus MC Transplant Institute, University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - David Wallace
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine and Institute of Liver Studies, King's College Hospital NHS Foundation Trust, London, UK
| | - Madhukar S Patel
- Division of Surgical Transplantation, Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Annabel Gravely
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada
| | - Woo Jin Choi
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada
| | - Chaya Shwaartz
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada
| | - Kate Walker
- Department of Nephrology and Transplantation, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Lauren Erdman
- The Centre of Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Gonzalo Sapisochin
- Multi-Organ Transplant Program, University Health Network Toronto, Ontario, Canada.
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30
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Rueda J, Rodríguez JD, Jounou IP, Hortal-Carmona J, Ausín T, Rodríguez-Arias D. "Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations. AI & SOCIETY 2022:1-12. [PMID: 36573157 PMCID: PMC9769482 DOI: 10.1007/s00146-022-01614-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022]
Abstract
The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps to maximize patients' benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources.
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Affiliation(s)
- Jon Rueda
- Department of Philosophy 1, University of Granada, Granada, Spain
- FiloLab Scientific Unit of Excellence, University of Granada, Granada, Spain
| | | | | | | | - Txetxu Ausín
- Institute of Philosophy, Spanish National Research Council, Madrid, Spain
| | - David Rodríguez-Arias
- Department of Philosophy 1, University of Granada, Granada, Spain
- FiloLab Scientific Unit of Excellence, University of Granada, Granada, Spain
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31
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Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching? MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121743. [PMID: 36556945 PMCID: PMC9783019 DOI: 10.3390/medicina58121743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.
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Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov. DISEASE MARKERS 2022; 2022:6847073. [PMID: 36193490 PMCID: PMC9526577 DOI: 10.1155/2022/6847073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 11/24/2022]
Abstract
Background Artificial intelligence (AI) has been widely applied in the diagnosis and therapy of chronic liver disease (CLD), but there is currently little insight into the trials registered on ClinicalTrials.gov. Thus, this cross-sectional study was focused on analyzing the progress in the use of AI in CLD. Methods Registered trials of AI applied in CLD on ClinicalTrials.gov were searched firstly. All available information was downloaded to Excel (Microsoft Excel, Rong, Rong, China), and duplicates were removed. We extracted the data of the included trials, then analyzed the characteristics of them finally. Results Up to the 27th of May 2021, 6835 trials were identified following an initial search, and 20 registered trials were included after screening for inclusion and exclusion criteria. Among those trials, hepatocellular carcinoma (HCC, 40.0%) and nonalcoholic fatty liver disease (NAFLD, 20.0%) were the most widely applied CLDs for AI. Trials started in 2013 until 2021, with 17 trials (85%) registered after 2016. There was a large trend in trial enrolment, with 40% of them including samples more than 500. Five trials (25%) have been completed, but only one of these had available results. The most frequent sponsors and collaborators were both hospitals at 55%, followed by universities at 35% and institutes at 11%, respectively. Of the 20 trials included, 35% (7 trials) were interventional trials and 65% (13 trials) were observational trials. Among 7 interventional trials, most trials were for diagnosis purpose (42.86%, 3 trials); 4 trials (57.14%) were randomized; 3 trials (42.86%) applied behavioral intervention, 1 trial (14.29%) was in device intervention, 2 trials (28.57%) were in diagnostic test, and 1 trial intervention was unknown. Among 13 observational trials, 8 (61.54%) were cohort studies; 6 (46.15%) were prospective studies, 4 (30.77%) were retrospective studies, 2 (15.38%) were cross-sectional studies, and 1 (7.69%) did not involve a temporal perspective. Conclusion The study is the first to focus on AI registration trials in CLD, which will aid relevant scholars in understanding the current state of the subject. This study demonstrates that additional research on AI used in the diagnosis and treatment of CLD is required, and timely publication of accessible results from registered trials is essential.
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Briceño J, Calleja R, Hervás C. Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing. Hepatobiliary Pancreat Dis Int 2022; 21:347-353. [PMID: 35321836 DOI: 10.1016/j.hbpd.2022.03.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/28/2022] [Indexed: 02/07/2023]
Abstract
Decision-making based on artificial intelligence (AI) methodology is increasingly present in all areas of modern medicine. In recent years, models based on deep-learning have begun to be used in organ transplantation. Taking into account the huge number of factors and variables involved in donor-recipient (D-R) matching, AI models may be well suited to improve organ allocation. AI-based models should provide two solutions: complement decision-making with current metrics based on logistic regression and improve their predictability. Hundreds of classifiers could be used to address this problem. However, not all of them are really useful for D-R pairing. Basically, in the decision to assign a given donor to a candidate in waiting list, a multitude of variables are handled, including donor, recipient, logistic and perioperative variables. Of these last two, some of them can be inferred indirectly from the team's previous experience. Two groups of AI models have been used in the D-R matching: artificial neural networks (ANN) and random forest (RF). The former mimics the functional architecture of neurons, with input layers and output layers. The algorithms can be uni- or multi-objective. In general, ANNs can be used with large databases, where their generalizability is improved. However, they are models that are very sensitive to the quality of the databases and, in essence, they are black-box models in which all variables are important. Unfortunately, these models do not allow to know safely the weight of each variable. On the other hand, RF builds decision trees and works well with small cohorts. In addition, they can select top variables as with logistic regression. However, they are not useful with large databases, due to the extreme number of decision trees that they would generate, making them impractical. Both ANN and RF allow a successful donor allocation in over 80% of D-R pairing, a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease, balance of risk score, and survival outcomes following liver transplantation scores. Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching. The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.
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Affiliation(s)
- Javier Briceño
- Unit of Liver Transplantation, Department of General Surgery, Hospital Universitario Reina Sofía, Córdoba, Spain; Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain.
| | - Rafael Calleja
- Unit of Liver Transplantation, Department of General Surgery, Hospital Universitario Reina Sofía, Córdoba, Spain; Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain
| | - César Hervás
- Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain; Department of Computer Sciences and Numerical Analysis, Universidad de Córdoba, Córdoba, Spain
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34
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Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, Orchanian-Cheff A, Wang B, Goldenberg A, Chassé M, Cardinal H, Cohen JP, Lodi A, Dieude M, Bhat M. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med 2022; 5:89. [PMID: 35817953 PMCID: PMC9273640 DOI: 10.1038/s41746-022-00637-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.
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Affiliation(s)
- Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Amirhossein Azhie
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Divya Sharma
- Department of Gastroenterology, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Ashley Spann
- Division of Gastroenterology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nan-Ji Suo
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Jason Tran
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Ani Orchanian-Cheff
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Bo Wang
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Anna Goldenberg
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Michael Chassé
- Department of Medicine (Critical Care), University of Montreal Hospital, Montréal, QC, Canada.,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada
| | - Heloise Cardinal
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada
| | - Joseph Paul Cohen
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.,Mila, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Andrea Lodi
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Canada Excellence Research Chair, Polytechnique Montréal, Montréal, QC, Canada
| | - Melanie Dieude
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada.,Department Microbiology, Infectiology and Immunology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.,Héma-Québec, Montréal, QC, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada. .,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada. .,Division of Gastroenterology and Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
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35
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Peloso A, Moeckli B, Delaune V, Oldani G, Andres A, Compagnon P. Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation. Transpl Int 2022; 35:10640. [PMID: 35859667 PMCID: PMC9290190 DOI: 10.3389/ti.2022.10640] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.
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Affiliation(s)
- Andrea Peloso
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- *Correspondence: Andrea Peloso,
| | - Beat Moeckli
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Vaihere Delaune
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Graziano Oldani
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Axel Andres
- Department of General Surgery, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
| | - Philippe Compagnon
- Department of Transplantation, University of Geneva Hospitals, University of Geneva, Geneva, Switzerland
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36
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Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int 2022; 16:495-508. [PMID: 35020154 DOI: 10.1007/s12072-021-10291-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
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Affiliation(s)
- Jason Tran
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Divya Sharma
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
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37
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Are MELD and MELDNa Still Reliable Tools to Predict Mortality on the Liver Transplant Waiting List? Transplantation 2022; 106:2122-2136. [PMID: 35594480 DOI: 10.1097/tp.0000000000004163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Liver transplantation is the only curative treatment for end-stage liver disease. Unfortunately, the scarcity of donor organs and the increasing pool of potential recipients limit access to this life-saving procedure. Allocation should account for medical and ethical factors, ensuring equal access to transplantation regardless of recipient's gender, race, religion, or income. Based on their short-term prognosis prediction, model for end-stage liver disease (MELD) and MELD sodium (MELDNa) have been widely used to prioritize patients on the waiting list for liver transplantation resulting in a significant decrease in waiting list mortality/removal. Recent concern has been raised regarding the prognostic accuracy of MELD and MELDNa due, in part, to changes in recipients' profile such as body mass index, comorbidities, and general condition, including nutritional status and cause of liver disease, among others. This review aims to provide a comprehensive view of the current state of MELD and MELDNa advantages and limitations and promising alternatives. Finally, it will explore future options to increase the donor pool and improve donor-recipient matching.
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38
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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39
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Wu T, Simonetto DA, Halamka JD, Shah VH. The digital transformation of hepatology: The patient is logged in. Hepatology 2022; 75:724-739. [PMID: 35028960 PMCID: PMC9531185 DOI: 10.1002/hep.32329] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/14/2022]
Abstract
The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient-centric medicine that is reaching beyond traditional brick-and-mortar facilities into patients' homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health-related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision-making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.
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Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - John D. Halamka
- Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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40
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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41
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Mucenic M, de Mello Brandão AB, Marroni CA. Artificial intelligence and human liver allocation: Potential benefits and ethical implications. Artif Intell Gastroenterol 2022; 3:21-27. [DOI: 10.35712/aig.v3.i1.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/13/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
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42
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Yang M, Peng B, Zhuang Q, Li J, Liu H, Cheng K, Ming Y. Models to predict the short-term survival of acute-on-chronic liver failure patients following liver transplantation. BMC Gastroenterol 2022; 22:80. [PMID: 35196992 PMCID: PMC8867783 DOI: 10.1186/s12876-022-02164-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 02/15/2022] [Indexed: 12/23/2022] Open
Abstract
Background Acute-on-chronic liver failure (ACLF) is featured with rapid deterioration of chronic liver disease and poor short-term prognosis. Liver transplantation (LT) is recognized as the curative option for ACLF. However, there is no standard in the prediction of the short-term survival among ACLF patients following LT. Method Preoperative data of 132 ACLF patients receiving LT at our center were investigated retrospectively. Cox regression was performed to determine the risk factors for short-term survival among ACLF patients following LT. Five conventional score systems (the MELD score, ABIC, CLIF-C OFs, CLIF-SOFAs and CLIF-C ACLFs) in forecasting short-term survival were estimated through the receiver operating characteristic (ROC). Four machine-learning (ML) models, including support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP) and random forest (RF), were also established for short-term survival prediction. Results Cox regression analysis demonstrated that creatinine (Cr) and international normalized ratio (INR) were the two independent predictors for short-term survival among ACLF patients following LT. The ROC curves showed that the area under the curve (AUC) ML models was much larger than that of conventional models in predicting short-term survival. Among conventional models the model for end stage liver disease (MELD) score had the highest AUC (0.704), while among ML models the RF model yielded the largest AUC (0.940). Conclusion Compared with the traditional methods, the ML models showed good performance in the prediction of short-term prognosis among ACLF patients following LT and the RF model perform the best. It is promising to optimize organ allocation and promote transplant survival based on the prediction of ML models. Supplementary Information The online version contains supplementary material available at 10.1186/s12876-022-02164-6.
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Affiliation(s)
- Min Yang
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Bo Peng
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Quan Zhuang
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Junhui Li
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Hong Liu
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Ke Cheng
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China
| | - Yingzi Ming
- Transplantation Center, Third Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
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Henn J, Buness A, Schmid M, Kalff JC, Matthaei H. Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review. Langenbecks Arch Surg 2022; 407:51-61. [PMID: 34716472 PMCID: PMC8847247 DOI: 10.1007/s00423-021-02348-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 10/03/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery. METHODS Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed. RESULTS Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM. CONCLUSIONS A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.
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Affiliation(s)
- Jonas Henn
- Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany
| | - Andreas Buness
- Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Jörg C Kalff
- Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany
| | - Hanno Matthaei
- Department of General, Visceral, Thoracic and Vascular Surgery, University of Bonn, Bonn, Germany.
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Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13:1977-1990. [PMID: 35070002 PMCID: PMC8727218 DOI: 10.4254/wjh.v13.i12.1977] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/09/2021] [Accepted: 11/25/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.
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Affiliation(s)
- Fadl H Veerankutty
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Govind Jayan
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Manish Kumar Yadav
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Krishnan Sarojam Manoj
- Department of Radiodiagnosis, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Abhishek Yadav
- Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India
| | - Sindhu Radha Sadasivan Nair
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - T U Shabeerali
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Varghese Yeldho
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
| | - Madhu Sasidharan
- Gastroenterology and Hepatology, Kerala Institute of Medical Sciences, Thiruvananthapuram 695029, India
| | - Shiraz Ahmad Rather
- Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India
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Khorsandi SE, Hardgrave HJ, Osborn T, Klutts G, Nigh J, Spencer-Cole RT, Kakos CD, Anastasiou I, Mavros MN, Giorgakis E. Artificial Intelligence in Liver Transplantation. Transplant Proc 2021; 53:2939-2944. [PMID: 34740449 DOI: 10.1016/j.transproceed.2021.09.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/30/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence. METHOD/RESULTS All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naïve Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology. CONCLUSION In the years to come, deep learning-based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.
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Affiliation(s)
- Shirin Elizabeth Khorsandi
- Institute of Liver Studies, King's College Hospital, Denmark Hill, London, UK; Institute of Hepatology, Foundation for Liver Research, Denmark Hill, London, UK; Faculty of Life Sciences & Medicine, King's College London, Strand, London, UK
| | - Hailey J Hardgrave
- College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Tamara Osborn
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | - Garrett Klutts
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | - Joe Nigh
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | | | - Christos D Kakos
- Surgery Working Group, Society of Junior Doctors, Athens, Greece
| | - Ioannis Anastasiou
- Department of Medicine, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas
| | - Michail N Mavros
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas; Surgical Oncology, University of Arkansas for Medical Sciences Winthrop P. Rockefeller Cancer Institute, Little Rock, Arkansas
| | - Emmanouil Giorgakis
- Department of Surgery, University of Arkansas for Medical Sciences Medical Center, Little Rock, Arkansas; Surgical Oncology, University of Arkansas for Medical Sciences Winthrop P. Rockefeller Cancer Institute, Little Rock, Arkansas.
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Taherkhani N, Sepehri MM, Khasha R, Shafaghi S. Determining the Level of Importance of Variables in Predicting Kidney Transplant Survival Based on a Novel Ranking Method. Transplantation 2021; 105:2307-2315. [PMID: 33534528 DOI: 10.1097/tp.0000000000003623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Kidney transplantation is the best alternative treatment for end-stage renal disease. To optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival. METHODS Five classification models were used to classify kidney recipients in long- and short-term survival classes. Synthetic minority oversampling and random undersampling were used to overcome the imbalanced class problem. In dealing with missing values, 2 approaches were used (eliminating and imputing them). All variables were categorized into 4 levels. The ranking was evaluated using the sensitivity analysis approach. RESULTS Thirty-four of the 41 variables were identified as important variables, of which, 5 variables were categorized in very important level ("Recipient creatinine at discharge," "Recipient dialysis time," "Donor history of diabetes," "Donor kidney biopsy," and "Donor cause of death"), 17 variables in important level, and 12 variables in the low important level. CONCLUSIONS In this study, we identify new variables that have not been addressed in any of the previous studies (eg, AGE_DIF and MATCH_GEN). On the other hand, in kidney allocation systems, 2 main criteria are considered: equity and utility. One of the utility subcriteria is the graft survival. Our study findings can be used in the design of systems to predict the graft survival.
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Affiliation(s)
- Nasrin Taherkhani
- Faculty Member of Computer Engineering, Payam-e-Noor University, Saveh, Iran
| | - Mohammad Mehdi Sepehri
- Department of Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Roghaye Khasha
- Center of Excellence in Healthcare Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Shadi Shafaghi
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021; 73:2546-2563. [PMID: 33098140 DOI: 10.1002/hep.31603] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/15/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022]
Abstract
Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
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Affiliation(s)
- Joseph C Ahn
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | | | | | | | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
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