|
1
|
Zhixing L, Linsen Y, Peng J, Siyi D, Haoyuan Y, Kun L, Siqi L, Yongwei H, Mingshen Z, Wei L, Hua L, Shuhong Y, Guihua C, Xiao X, Shusen Z, Yang Y. Explainable machine learning for the assessment of donor grafts in liver transplantation. Hepatol Res 2025. [PMID: 40317606 DOI: 10.1111/hepr.14187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Revised: 02/16/2025] [Accepted: 03/07/2025] [Indexed: 05/07/2025]
Abstract
BACKGROUND AND AIM The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine learning (ML) model to predict postoperative delayed graft function (DGF) and visualize the decision-making process for clinical application. METHOD Data from 5242 donor-recipient pairs who underwent liver transplantation (LT) at the top 10 liver transplant centers in China (January 2017 to December 2022) were collected. The dataset was divided into training and validation sets. Sixty-three variables, including demographics, donor characteristics, diagnosis, preoperative lab results, and surgical information were analyzed. The primary outcome was posttransplantation DGF and the second outcome was posttransplantation 1-month and 3-month survival. Recursive feature elimination selected critical variables, and models were built using ML algorithms and logistic regression. Model performance was evaluated by AUC, accuracy, sensitivity, and specificity. The best model was validated with an independent dataset of 394 LT cases (January to June 2023). The SHapley Additive exPlanations package interpreted the top model's decisions. RESULTS Among 5242 cases, 328 (6.26%) developed DGF, with 15 cases (3.81%) in the external validation set. Thirty critical features were selected. The eXtreme Gradient Boosting algorithm achieved the highest AUC (0.877) and accuracy (0.936) in the internal set, and a comparable AUC (0.776) and accuracy (0.957) in the external set. SHAP analysis identified short perfusion time, high donor serum sodium, excessive bleeding during transplantation, high donor γ-glutamyl transpeptidase, and blood glucose levels as top predictors of post-LT DGF. The proposed model AUC's 1-month survival prediction was 0.841 and the 3-month survival prediction was 0.834. CONCLUSIONS The developed model for predicting postoperative DGF demonstrated excellent predictive performance, aiding clinicians in evaluating donor grafts and making informed decisions.
Collapse
Affiliation(s)
- Liang Zhixing
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ye Linsen
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiang Peng
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Dong Siyi
- National Center for Healthcare Quality Management of Liver Transplant, Hangzhou, China
| | - Yu Haoyuan
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Li Kun
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Li Siqi
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hu Yongwei
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhang Mingshen
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liu Wei
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Li Hua
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yi Shuhong
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Chen Guihua
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xu Xiao
- National Center for Healthcare Quality Management of Liver Transplant, Hangzhou, China
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
- School of Clinical Medicine, Hangzhou Medical College, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
| | - Zheng Shusen
- National Center for Healthcare Quality Management of Liver Transplant, Hangzhou, China
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
- Department of Hepatobiliary Surgery, First Affiliated Hospital of Zhejiang University, Hangzhou, China
- Department of Hepatobiliary and Pancreatic Surgery, Shulan (Hangzhou) Hospital, Hangzhou, China
| | - Yang Yang
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
|
2
|
Tay JL, Ang YL, Tam WWS, Sim K. Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review. BMJ Open 2025; 15:e084463. [PMID: 40000074 DOI: 10.1136/bmjopen-2024-084463] [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] [Indexed: 02/27/2025] Open
Abstract
OBJECTIVES We aimed to examine the predictive accuracy of functioning, relapse or remission among patients with psychotic disorders, using machine learning methods. We also identified specific features that were associated with these clinical outcomes. DESIGN The methodology of this review was guided by the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. DATA SOURCES CINAHL, EMBASE, PubMed, PsycINFO, Scopus and ScienceDirect were searched for relevant articles from database inception until 21 November 2024. ELIGIBILITY CRITERIA Studies were included if they involved the use of machine learning methods to predict functioning, relapse and/or remission among individuals with psychotic spectrum disorders. DATA EXTRACTION AND SYNTHESIS Two independent reviewers screened the records from the database search. Risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies tool from Cochrane. Synthesised findings were presented in tables. RESULTS 23 studies were included in the review, which were mostly conducted in the west (91%). Predictive summary area under the curve values for functioning, relapse and remission were 0.63-0.92 (poor to outstanding), 0.45-0.95 (poor to outstanding), 0.70-0.79 (acceptable), respectively. Logistic regression and random forest were the best performing algorithms. Factors influencing outcomes included demographic (age, ethnicity), illness (duration of untreated illness, types of symptoms), functioning (baseline functioning, interpersonal relationships and activity engagement), treatment variables (use of higher doses of antipsychotics, electroconvulsive therapy), data from passive sensor (call log, distance travelled, time spent in certain locations) and online activities (time of use, use of certain words, changes in search frequencies and length of queries). CONCLUSION Machine learning methods show promise in the prediction of prognosis (specifically functioning, relapse and remission) of mental disorders based on relevant collected variables. Future machine learning studies may want to focus on the inclusion of a broader swathe of variables including ecological momentary assessments, with a greater amount of good quality big data covering longer longitudinal illness courses and coupled with external validation of study findings. PROSPERO REGISTRATION NUMBER The review was registered on PROSPERO, ID: CRD42023441108.
Collapse
Affiliation(s)
| | - Yun Ling Ang
- Department of Nursing, Institute of Mental Health, Singapore
| | - Wilson W S Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| |
Collapse
|
|
3
|
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.
Collapse
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
| |
Collapse
|
|
4
|
Olawade DB, Marinze S, Qureshi N, Weerasinghe K, Teke J. The impact of artificial intelligence and machine learning in organ retrieval and transplantation: A comprehensive review. Curr Res Transl Med 2025; 73:103493. [PMID: 39792149 DOI: 10.1016/j.retram.2025.103493] [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: 09/23/2024] [Revised: 12/11/2024] [Accepted: 01/05/2025] [Indexed: 01/12/2025]
Abstract
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency.
Collapse
Affiliation(s)
- David B Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Department of Public Health, York St John University, London, United Kingdom; School of Health and Care Management, Arden University, Arden House, Middlemarch Park, Coventry CV3 4FJ, United Kingdom.
| | - Sheila Marinze
- Department of Surgery, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
| | - Nabeel Qureshi
- Department of Surgery, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
| | - Kusal Weerasinghe
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
| | - Jennifer Teke
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom
| |
Collapse
|
|
5
|
Mohamud KA, Elzubair Eltahir SA, Ahmed Alhardalo HA, Albashir HB, Ali Mohamed Zain NQA, Abdelrahman Ibrahim ME, Ahmed Fadlallah EN. The Role of Machine Learning Models in Predicting Cirrhosis Mortality: A Systematic Review. Cureus 2025; 17:e78155. [PMID: 40026938 PMCID: PMC11867977 DOI: 10.7759/cureus.78155] [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] [Accepted: 01/28/2025] [Indexed: 03/05/2025] Open
Abstract
Liver cirrhosis affects millions of individuals worldwide and is one of the primary causes of mortality. Early mortality prediction for cirrhosis patients may increase the possibility for medical professionals to treat the illness successfully. This study assesses the ability of machine learning (ML) models to predict cirrhosis mortality. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant literature across four different databases. We found 379 studies of which 10 were eligible for inclusion in the current study. We analyzed 10 retrospective studies that showed that ML models outperformed conventional scores in predicting the death rate from end-stage liver disease (ESLD). Interestingly, models that used more parameters, such as patient demographics and extensive laboratory testing, exhibited higher prediction accuracy. With an area under the receiver operating characteristic (AUROC) ranging from 0.71 to 0.96, ML models showed consistently significant gains over traditional prognostic ratings. This review emphasizes how ML models might improve ESLD patient death prediction. Because machine learning models are more accurate than conventional approaches, it is important to incorporate data-driven informatics technologies into clinical settings. Additional validation and openness are required to guarantee model dependability and interpretability before ML may be used in clinical practice. The goal of future research should be to create reliable, interpretable models that may be used successfully in a variety of clinical contexts, enhancing ESLD patient treatment and results.
Collapse
Affiliation(s)
| | | | - Hind AbdAlla Ahmed Alhardalo
- Department of General Medicine, Abu Dhabi Health Services Company (SEHA) - Salma Rehabilitation Hospital, Abu Dhabi, ARE
| | - Hadel Bakhet Albashir
- Department of General Medicine, Abu Dhabi Health Services Company (SEHA) - Salma Rehabilitation Hospital, Abu Dhabi, ARE
| | | | | | | |
Collapse
|
|
6
|
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.
Collapse
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
| |
Collapse
|
|
7
|
Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. Artif Intell Cancer 2024; 5. [DOI: 10.35713/aic.v5.i2.97317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 09/05/2024] Open
Abstract
Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.
Collapse
Affiliation(s)
- Ankit Shukla
- Department of Surgery, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| | - Rajesh Chaudhary
- Department of Renal Transplantation, Dr Rajendra Prasad Government Medical College, Kangra 176001, India
| | - Nishant Nayyar
- Department of Radiology, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| |
Collapse
|
|
8
|
Feng S, Roll GR, Rouhani FJ, Sanchez Fueyo A. The future of liver transplantation. Hepatology 2024; 80:674-697. [PMID: 38537154 DOI: 10.1097/hep.0000000000000873] [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: 01/26/2024] [Accepted: 03/02/2024] [Indexed: 06/15/2024]
Abstract
Over the last 50 years, liver transplantation has evolved into a procedure routinely performed in many countries worldwide. Those able to access this therapy frequently experience a miraculous risk-benefit ratio, particularly if they face the imminently life-threatening disease. Over the decades, the success of liver transplantation, with dramatic improvements in early posttransplant survival, has aggressively driven demand. However, despite the emergence of living donors to augment deceased donors as a source of organs, supply has lagged far behind demand. As a result, rationing has been an unfortunate focus in recent decades. Recent shifts in the epidemiology of liver disease combined with transformative innovations in liver preservation suggest that the underlying premise of organ shortage may erode in the foreseeable future. The focus will sharpen on improving equitable access while mitigating constraints related to workforce training, infrastructure for organ recovery and rehabilitation, and their associated costs. Research efforts in liver preservation will undoubtedly blossom with the aim of optimizing both the timing and conditions of transplantation. Coupled with advances in genetic engineering, regenerative biology, and cellular therapies, the portfolio of innovation, both broad and deep, offers the promise that, in the future, liver transplantation will not only be broadly available to those in need but also represent a highly durable life-saving therapy.
Collapse
Affiliation(s)
- Sandy Feng
- Department of Surgery, Division of Transplant Surgery, University of California, San Francisco, California, USA
| | - Garrett R Roll
- Department of Surgery, Division of Transplant Surgery, University of California, San Francisco, California, USA
| | - Foad J Rouhani
- Tissue Regeneration and Clonal Evolution Laboratory, The Francis Crick Institute, London, UK
- Institute of Liver Studies, King's College London, King's College Hospital, NHS Foundation Trust, London, UK
| | - Alberto Sanchez Fueyo
- Institute of Liver Studies, King's College London, King's College Hospital, NHS Foundation Trust, London, UK
| |
Collapse
|
|
9
|
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.
Collapse
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
| |
Collapse
|
|
10
|
Gulla A, Jakiunaite I, Juchneviciute I, Dzemyda G. A narrative review: predicting liver transplant graft survival using artificial intelligence modeling. FRONTIERS IN TRANSPLANTATION 2024; 3:1378378. [PMID: 38993758 PMCID: PMC11235265 DOI: 10.3389/frtra.2024.1378378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/22/2024] [Indexed: 07/13/2024]
Abstract
Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.
Collapse
Affiliation(s)
- Aiste Gulla
- Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, Vilnius, Lithuania
| | | | - Ivona Juchneviciute
- Faculty of Mathematics and Informatics, Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
| | - Gintautas Dzemyda
- Faculty of Mathematics and Informatics, Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania
| |
Collapse
|
|
11
|
Meszaros AT, Weissenbacher A, Schartner M, Egelseer-Bruendl T, Hermann M, Unterweger J, Mittelberger C, Reyer BA, Hofmann J, Zelger BG, Hautz T, Resch T, Margreiter C, Maglione M, Komlódi T, Ulmer H, Cardini B, Troppmair J, Öfner D, Gnaiger E, Schneeberger S, Oberhuber R. The Predictive Value of Graft Viability and Bioenergetics Testing Towards the Outcome in Liver Transplantation. Transpl Int 2024; 37:12380. [PMID: 38463463 PMCID: PMC10920229 DOI: 10.3389/ti.2024.12380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 02/12/2024] [Indexed: 03/12/2024]
Abstract
Donor organ biomarkers with sufficient predictive value in liver transplantation (LT) are lacking. We herein evaluate liver viability and mitochondrial bioenergetics for their predictive capacity towards the outcome in LT. We enrolled 43 consecutive patients undergoing LT. Liver biopsy samples taken upon arrival after static cold storage were assessed by histology, real-time confocal imaging analysis (RTCA), and high-resolution respirometry (HRR) for mitochondrial respiration of tissue homogenates. Early allograft dysfunction (EAD) served as primary endpoint. HRR data were analysed with a focus on the efficacy of ATP production or P-L control efficiency, calculated as 1-L/P from the capacity of oxidative phosphorylation P and non-phosphorylating respiration L. Twenty-two recipients experienced EAD. Pre-transplant histology was not predictive of EAD. The mean RTCA score was significantly lower in the EAD cohort (-0.75 ± 2.27) compared to the IF cohort (0.70 ± 2.08; p = 0.01), indicating decreased cell viability. P-L control efficiency was predictive of EAD (0.76 ± 0.06 in IF vs. 0.70 ± 0.08 in EAD-livers; p = 0.02) and correlated with the RTCA score. Both RTCA and P-L control efficiency in biopsy samples taken during cold storage have predictive capacity towards the outcome in LT. Therefore, RTCA and HRR should be considered for risk stratification, viability assessment, and bioenergetic testing in liver transplantation.
Collapse
Affiliation(s)
- Andras T. Meszaros
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Annemarie Weissenbacher
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Melanie Schartner
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Tim Egelseer-Bruendl
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Martin Hermann
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Jasmin Unterweger
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Christa Mittelberger
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Beatrix A. Reyer
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Julia Hofmann
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Bettina G. Zelger
- Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
| | - Theresa Hautz
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Thomas Resch
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Christian Margreiter
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Manuel Maglione
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Hanno Ulmer
- Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Innsbruck, Austria
| | - Benno Cardini
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Jakob Troppmair
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Dietmar Öfner
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Stefan Schneeberger
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| | - Rupert Oberhuber
- Department of Visceral, Transplant and Thoracic Surgery, Medical University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
|
12
|
Wingfield LR, Salaun A, Khan A, Webb H, Zhu T, Knight S. Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review. Transplantation 2024; 108:72-99. [PMID: 37143191 DOI: 10.1097/tp.0000000000004627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.
Collapse
Affiliation(s)
- Laura R Wingfield
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Achille Salaun
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aparajita Khan
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Helena Webb
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Simon Knight
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
|
13
|
Sharma P, Arora A. Basic Understanding of Liver Transplant Immunology. J Clin Exp Hepatol 2023; 13:1091-1102. [PMID: 37975047 PMCID: PMC10643508 DOI: 10.1016/j.jceh.2023.05.007] [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: 03/08/2023] [Accepted: 05/14/2023] [Indexed: 11/19/2023] Open
Abstract
The liver is a specialized organ and plays an important role in our immune system. The liver constitutes parenchymal cells which are hepatocytes and cholangiocytes (60-80%) and non-parenchymal cells like liver sinusoidal endothelial cells (LSECs), hepatic satellite/Ito cells, Kupffer cells, neutrophils, mononuclear cells, T and B lymphocytes (conventional and non-conventional), natural killer cells, and natural killer T (NKT) cells. The liver mounts a rapid and strong immune response, under unfavorable conditions and acts as an immune tolerance to a variety of non-pathogenic antigens. This delicate and dynamic interaction between different kinds of immune cells in the liver maintains a balance between immune screening and immune tolerance. The liver allografts are privileged immunologically; however, allograft rejection is not uncommon and is classified as cell or antibody-mediated. Advancements in transplant immunology help in the prevention of allografts rejection by immune reactions of the host thus leading to better graft and host survival. Fewer patients may not require immunosuppression due to systemic donor-specific T-cell tolerance. The liver tolerance mechanism is poorly studied, and LSEC and unconventional lymphocytes play an important role that dampens T cell response either by inducing apoptosis of cells or inhibiting co-stimulatory pathways. Newer cell-based therapy based on Treg, dendritic cells, and mesenchymal stromal cells will probably change the future of immunosuppression. Various invasive and non-invasive biomarkers and artificial intelligence have also been investigated to predict graft survival, post-transplant complications, and immunotolerance in the future.
Collapse
Affiliation(s)
- Praveen Sharma
- Department of Gastroenterology, Sir Ganga Ram Hospital, New Delhi, India
| | - Anil Arora
- Department of Gastroenterology and Hepatology, Sir Ganga Ram Hospital, New Delhi, India
| |
Collapse
|
|
14
|
Wehkamp K, Krawczak M, Schreiber S. The Quality and Utility of Artificial Intelligence in Patient Care. DEUTSCHES ARZTEBLATT INTERNATIONAL 2023; 120:463-469. [PMID: 37218054 PMCID: PMC10487679 DOI: 10.3238/arztebl.m2023.0124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 11/30/2022] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly being used in patient care. In the future, physicians will need to understand not only the basic functioning of AI applications, but also their quality, utility, and risks. METHODS This article is based on a selective review of the literature on the principles, quality, limitations, and benefits AI applications in patient care, along with examples of individual applications. RESULTS The number of AI applications in patient care is rising, with more than 500 approvals in the United States to date. Their quality and utility are based on a number of interdependent factors, including the real-life setting, the type and amount of data collected, the choice of variables used by the application, the algorithms used, and the goal and implementation of each application. Bias (which may be hidden) and errors can arise at all these levels. Any evaluation of the quality and utility of an AI application must, therefore, be conducted according to the scientific principles of evidence-based medicine-a requirement that is often hampered by a lack of transparency. CONCLUSION AI has the potential to improve patient care while meeting the challenge of dealing with an ever-increasing surfeit of information and data in medicine with limited human resources. The limitations and risks of AI applications require critical and responsible consideration. This can best be achieved through a combination of scientific.
Collapse
Affiliation(s)
- Kai Wehkamp
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Lübeck, Kiel, Germany
- Department for Medical Management, MSH Medical School Hamburg, Hamburg, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Christian-Albrechts-University of Kiel, University Medical Center Schleswig-Holstein Campus Kiel, Germany
| | - Stefan Schreiber
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Lübeck, Kiel, Germany
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, University Medical Center Schleswig-Holstein Campus Kiel, Germany
| |
Collapse
|
|
15
|
Schattenberg JM, Chalasani N, Alkhouri N. Artificial Intelligence Applications in Hepatology. Clin Gastroenterol Hepatol 2023; 21:2015-2025. [PMID: 37088460 DOI: 10.1016/j.cgh.2023.04.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/16/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023]
Abstract
Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.
Collapse
Affiliation(s)
- Jörn M Schattenberg
- Metabolic Liver Research Program, I. Department of Medicine, University Medical Center Mainz, Mainz, Germany
| | - Naga Chalasani
- Indiana University School of Medicine and Indiana University Health, Indianapolis, Indiana
| | - Naim Alkhouri
- Arizona Liver Health and University of Arizona, Tucson, Arizona.
| |
Collapse
|
|
16
|
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.
Collapse
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
| |
Collapse
|
|
17
|
Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023; 78:1216-1233. [PMID: 37208107 DOI: 10.1016/j.jhep.2023.01.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 05/21/2023]
Abstract
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.
Collapse
Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Beatriz Sordi Chara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
|
18
|
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.
Collapse
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
| |
Collapse
|
|
19
|
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.
Collapse
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
| |
Collapse
|
|
20
|
Zhang X, Gavaldà R, Baixeries J. Interpretable prediction of mortality in liver transplant recipients based on machine learning. Comput Biol Med 2022; 151:106188. [PMID: 36306583 DOI: 10.1016/j.compbiomed.2022.106188] [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: 01/08/2022] [Revised: 09/24/2022] [Accepted: 10/08/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Accurate prediction of the mortality of post-liver transplantation is an important but challenging task. It relates to optimizing organ allocation and estimating the risk of possible dysfunction. Existing risk scoring models, such as the Balance of Risk (BAR) score and the Survival Outcomes Following Liver Transplantation (SOFT) score, do not predict the mortality of post-liver transplantation with sufficient accuracy. In this study, we evaluate the performance of machine learning models and establish an explainable machine learning model for predicting mortality in liver transplant recipients. METHOD The optimal feature set for the prediction of the mortality was selected by a wrapper method based on binary particle swarm optimization (BPSO). With the selected optimal feature set, seven machine learning models were applied to predict mortality over different time windows. The best-performing model was used to predict mortality through a comprehensive comparison and evaluation. An interpretable approach based on machine learning and SHapley Additive exPlanations (SHAP) is used to explicitly explain the model's decision and make new discoveries. RESULTS With regard to predictive power, our results demonstrated that the feature set selected by BPSO outperformed both the feature set in the existing risk score model (BAR score, SOFT score) and the feature set processed by principal component analysis (PCA). The best-performing model, extreme gradient boosting (XGBoost), was found to improve the Area Under a Curve (AUC) values for mortality prediction by 6.7%, 11.6%, and 17.4% at 3 months, 3 years, and 10 years, respectively, compared to the SOFT score. The main predictors of mortality and their impact were discussed for different age groups and different follow-up periods. CONCLUSIONS Our analysis demonstrates that XGBoost can be an ideal method to assess the mortality risk in liver transplantation. In combination with the SHAP approach, the proposed framework provides a more intuitive and comprehensive interpretation of the predictive model, thereby allowing the clinician to better understand the decision-making process of the model and the impact of factors associated with mortality risk in liver transplantation.
Collapse
Affiliation(s)
- Xiao Zhang
- Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain.
| | | | - Jaume Baixeries
- Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain
| |
Collapse
|
|
21
|
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.
Collapse
|
|
22
|
Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
Collapse
Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
| |
Collapse
|
|
23
|
Goldberg D, Mantero A, Kaplan D, Delgado C, John B, Nuchovich N, Emanuel E, Reese PP. Accurate long-term prediction of death for patients with cirrhosis. Hepatology 2022; 76:700-711. [PMID: 35278226 PMCID: PMC9378359 DOI: 10.1002/hep.32457] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 03/03/2022] [Accepted: 03/08/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS Cirrhosis is a major cause of death and is associated with extensive health care use. Patients with cirrhosis have complex treatment choices due to risks of morbidity and mortality. To optimally counsel and treat patients with cirrhosis requires tools to predict their longer-term liver-related survival. We sought to develop and validate a risk score to predict longer-term survival of patients with cirrhosis. APPROACH AND RESULTS We conducted a retrospective cohort study of adults with cirrhosis with no major life-limiting comorbidities. Adults with cirrhosis within the Veterans Health Administration were used for model training and internal validation, and external validation used the OneFlorida Clinical Research Consortium. We used four model-building approaches including variables predictive of cirrhosis-related mortality, focused on discrimination at key time points (1, 3, 5, and 10 years). Among 30,263 patients with cirrhosis ≤75 years old without major life-limiting comorbidities and complete laboratory data during the baseline period, the boosted survival tree models had the highest discrimination, with 1-year, 3-year, 5-year, and 10-year survival rates of 0.77, 0.81, 0.84, and 0.88, respectively. The 1-year, 3-year, and 5-year discrimination was nearly identical in external validation. Secondary analyses with imputation of missing data and subgroups by etiology of liver disease had similar results to the primary model. CONCLUSIONS We developed and validated (internally and externally) a risk score to predict longer-term survival of patients with cirrhosis. This score would transform management of patients with cirrhosis in terms of referral to specialty care and treatment decision-making for non-liver-related care.
Collapse
Affiliation(s)
- David Goldberg
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL
| | - Alejandro Mantero
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL
| | - David Kaplan
- Division of Gastroenterology, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Cindy Delgado
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Binu John
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
- Bruce Carter VA Medica Center, Miami, FL
| | - Nadine Nuchovich
- Division of Digestive Health and Liver Diseases, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL
| | - Ezekiel Emanuel
- Department of Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Peter P. Reese
- Renal-Electrolye and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| |
Collapse
|
|
24
|
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.
Collapse
|
|
25
|
Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58040459. [PMID: 35454298 PMCID: PMC9029673 DOI: 10.3390/medicina58040459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.
Collapse
Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123 Allschwil, Switzerland
- Correspondence:
| | - Vincent Ochs
- Roche Innovation Center Basel, Department of Pharma Research & Early Development, 4070 Basel, Switzerland;
| | - Leos N. Kayhan
- Department of Surgery, Canntonal Hospital Luzern, 6004 Luzern, Switzerland;
| | - Bassey Enodien
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | - Daniel M. Frey
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | | | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, 4002 Basel, Switzerland;
| |
Collapse
|
|
26
|
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
|
|
27
|
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.
Collapse
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
| |
Collapse
|
|
28
|
Threlkeld R, Ashiku L, Canfield C, Shank DB, Schnitzler MA, Lentine KL, Axelrod DA, Battineni ACR, Randall H, Dagli C. Reducing Kidney Discard With Artificial Intelligence Decision Support: the Need for a Transdisciplinary Systems Approach. CURRENT TRANSPLANTATION REPORTS 2021; 8:263-271. [PMID: 35059280 PMCID: PMC8727423 DOI: 10.1007/s40472-021-00351-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2021] [Indexed: 11/20/2022]
Abstract
PURPOSE OF REVIEW A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard. RECENT FINDINGS Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and transplant healthcare. A systems approach leverages techniques for visualizing the system architecture to support solution design from multiple perspectives. SUMMARY Developing a systems-based approach to AI decision support involves engaging in a cycle of documenting the system architecture, identifying pain points, developing prototypes, and validating the system. Early efforts have focused on describing process issues to prioritize tasks that would benefit from AI support.
Collapse
Affiliation(s)
- Richard Threlkeld
- Engineering Management & Systems Engineering, Missouri University of Science & Technology, 223 Engineering Management 600 W 14th St, MO 65409 Rolla, USA
| | - Lirim Ashiku
- Engineering Management & Systems Engineering, Missouri University of Science & Technology, 223 Engineering Management 600 W 14th St, MO 65409 Rolla, USA
| | - Casey Canfield
- Engineering Management & Systems Engineering, Missouri University of Science & Technology, 223 Engineering Management 600 W 14th St, MO 65409 Rolla, USA
| | - Daniel B. Shank
- Psychological Science, Missouri University of Science & Technology, Rolla, MO USA
| | | | | | | | | | - Henry Randall
- Saint Louis University Transplant Center, St. Louis, MO USA
| | - Cihan Dagli
- Engineering Management & Systems Engineering, Missouri University of Science & Technology, 223 Engineering Management 600 W 14th St, MO 65409 Rolla, USA
| |
Collapse
|
|
29
|
Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2:127-135. [DOI: 10.37126/aige.v2.i4.127] [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: 04/21/2021] [Revised: 06/05/2021] [Accepted: 07/19/2021] [Indexed: 02/06/2023] Open
|
|
30
|
Clement J, Maldonado AQ. Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant. Front Immunol 2021; 12:694222. [PMID: 34177958 PMCID: PMC8226178 DOI: 10.3389/fimmu.2021.694222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 05/27/2021] [Indexed: 11/19/2022] Open
Abstract
Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).
Collapse
Affiliation(s)
- Jeffrey Clement
- Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, MN, United States
| | | |
Collapse
|
|
31
|
Lovejoy CA, Alqahtani SA. AI in colonoscopy and beyond: On the cusp of clinical implementation? United European Gastroenterol J 2021; 9:525-526. [PMID: 33960666 PMCID: PMC8259269 DOI: 10.1002/ueg2.12076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/05/2021] [Indexed: 11/06/2022] Open
Affiliation(s)
| | - Saleh A. Alqahtani
- Liver Transplantation UnitKing Faisal Specialist Hospital & Research CenterRiyadhSaudi Arabia
- Division of Gastroenterology and HepatologyJohns Hopkins UniversityBaltimoreMarylandUSA
| |
Collapse
|
|
32
|
Wang RF, Fagelman EJ, Smith NK, Sakai T. Abdominal Organ Transplantation: Noteworthy Literature in 2020. Semin Cardiothorac Vasc Anesth 2021; 25:138-150. [PMID: 33845699 DOI: 10.1177/10892532211007256] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In 2020, we identified and screened over 490 peer-reviewed publications on pancreatic transplantation, over 500 on intestinal transplantation, and over 5000 on kidney transplantation. The liver transplantation section specially focused on clinical trials and systematic reviews published in 2020 and featured selected articles. This review highlights noteworthy literature pertinent to anesthesiologists and critical care physicians caring for patients undergoing abdominal organ transplantation. We explore a wide range of topics, including COVID-19 and organ transplantation, risk factors and outcomes, pain management, artificial intelligence, robotic donor surgery, and machine perfusion.
Collapse
Affiliation(s)
- Ryan F Wang
- The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Erica J Fagelman
- The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Natalie K Smith
- The Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Tetsuro Sakai
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| |
Collapse
|
|
33
|
Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg 2021; 13:7-18. [PMID: 33552391 PMCID: PMC7830072 DOI: 10.4240/wjgs.v13.i1.7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/08/2020] [Accepted: 12/17/2020] [Indexed: 02/06/2023] Open
Abstract
Over the past decade, enhanced preoperative imaging and visualization, improved delineation of the complex anatomical structures of the liver and pancreas, and intra-operative technological advances have helped deliver the liver and pancreatic surgery with increased safety and better postoperative outcomes. Artificial intelligence (AI) has a major role to play in 3D visualization, virtual simulation, augmented reality that helps in the training of surgeons and the future delivery of conventional, laparoscopic, and robotic hepatobiliary and pancreatic (HPB) surgery; artificial neural networks and machine learning has the potential to revolutionize individualized patient care during the preoperative imaging, and postoperative surveillance. In this paper, we reviewed the existing evidence and outlined the potential for applying AI in the perioperative care of patients undergoing HPB surgery.
Collapse
Affiliation(s)
- Hassaan Bari
- Department of HPB and Liver Transplantation Surgery, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
| | - Sharan Wadhwani
- Department of Radiology, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
| | - Bobby V M Dasari
- Department of HPB and Liver Transplantation Surgery, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
| |
Collapse
|
|
34
|
Sucher R, Sucher E. Artificial intelligence is poised to revolutionize human liver allocation and decrease medical costs associated with liver transplantation. Hepatobiliary Surg Nutr 2020; 9:679-681. [PMID: 33163524 DOI: 10.21037/hbsn-20-458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Robert Sucher
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital Leipzig, Leipzig, Germany
| | - Elisabeth Sucher
- Department of Gastroenterology, Division of Hepatology, University Hospital Leipzig, Leipzig, Germany
| |
Collapse
|