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Soukup T, Zamora-Talaya B, Bahadori S, Luxardo R, Kierkegaard P, Butt O, Kettley-Linsell H, Savva KV, Micocci M, Zhou S, Newman S, Walne S, Peters CJ, Gordon A, Ni M, Buckle P, Hanna GB. Defining the value proposition in diagnostic technology: challenges and opportunities for its understanding and development - a review with a multiperspective reflective analysis. Front Med (Lausanne) 2025; 12:1498618. [PMID: 40051729 PMCID: PMC11884263 DOI: 10.3389/fmed.2025.1498618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2024] [Accepted: 01/20/2025] [Indexed: 03/09/2025] Open
Abstract
Background The Value Proposition (VP) in diagnostic technology serves as a "positioning statement" outlining the unique benefits, costs, and differentiation an innovation under development offers to healthcare organizations and its ability to effectively deliver these advantages in comparison to current interventions in the market. Despite its significance however, VP lacks a universally accepted definition, which is compounded by the diversity of technologies, their applications, and the varying needs of stakeholders. This paper aims to address this gap by offering a detailed conceptual analysis, revised definition of VP, and actionable recommendations for advancing VP development. Methodology We conducted a targeted narrative review, focusing on literature explicitly defining VPs in diagnostic technologies. Using Ovid's Medline and Embase databases, we identified 19 relevant papers, of which only 5 provided explicit VP definitions. Our analysis incorporated principles of team science, encompassing reflective and thematic analyses of (1) interdisciplinary co-author discussions enabling us to weave together diverse insights into a cohesive exploration of the topic, and (2) MTech's publicly available set of anonymised responses from NHS Associates, to capture the perspectives of the decision-makers and further enhance depth and breadth of our discourse. Results and discussion Our findings highlight the multifaceted nature of VP and its primary hurdles: inadequate identification of unmet needs and insufficient recognition of key stakeholders. We synthesized the evolution of VP definitions and explored the importance of unmet needs in their development, guided by frameworks, such as the Health Technology Navigation Pathway Tool, to ensure VPs meet both the pragmatic and aspirational goals of the healthcare. Thematic insights revealed opportunities for addressing these barriers through implementation science and collaborative strategies. This multi-perspective approach provided a conceptual examination of VP, enabling integration of varied viewpoints and insights. Conclusion By employing team science principles and reflective analysis, we introduced a revised definition of VP and a set of actionable recommendations to guide VP development in diagnostics. These findings highlight the importance of addressing stakeholder diversity, unmet needs, and the intricacies of blending interdisciplinary perspectives to advance the field.
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Affiliation(s)
- Tayana Soukup
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Bernarda Zamora-Talaya
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Shayan Bahadori
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Rosario Luxardo
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Patrick Kierkegaard
- Cancer Research UK Convergence Science Centre at The Institute of Cancer Research, London, and Imperial College London, London, United Kingdom
| | - Omar Butt
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Hannah Kettley-Linsell
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Katerina-Vanessa Savva
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Massimo Micocci
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Shanshan Zhou
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Simon Newman
- Royal National Orthopaedic Hospital NHS Trust, London, United Kingdom
| | - Simon Walne
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Christopher J. Peters
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Adam Gordon
- Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
- Academic Centre for Healthy Ageing, Barts Health NHS Trust, London, United Kingdom
| | - Melody Ni
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Peter Buckle
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - George B. Hanna
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
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McCarty TR, Shah R, Allencherril RP, Moon N, Njei B. The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis. J Clin Gastroenterol 2025:00004836-990000000-00421. [PMID: 39998988 DOI: 10.1097/mcg.0000000000002148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 01/22/2025] [Indexed: 02/27/2025]
Abstract
BACKGROUND Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.
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Affiliation(s)
- Thomas R McCarty
- Houston Methodist Hospital, Lynda K. and David M. Underwood Center for Digestive Disorders, Houston, TX
- Weill Cornell Medical College, New York, NY
- Texas A&M University, School of Medicine, Bryan College Station, TX
| | - Raj Shah
- The Ohio State University, Wexler School of Medicine, Division of Gastroenterology, Hepatology, and Nutrition, Columbus, OH
| | - Ronan P Allencherril
- Houston Methodist Hospital, Lynda K. and David M. Underwood Center for Digestive Disorders, Houston, TX
| | - Nabeel Moon
- Houston Methodist Hospital, Lynda K. and David M. Underwood Center for Digestive Disorders, Houston, TX
| | - Basile Njei
- Yale University School of Medicine, Investigative Medicine Program, Yale Institute for Global Health, Section of Digestive Diseases, New Haven, CT
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Wang HN, An JH, Zong L. Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicine. World J Gastrointest Oncol 2024; 16:4548-4552. [PMID: 39678793 PMCID: PMC11577357 DOI: 10.4251/wjgo.v16.i12.4548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 08/05/2024] [Accepted: 08/12/2024] [Indexed: 11/12/2024] Open
Abstract
Survival rates following radical surgery for gastric neuroendocrine neoplasms (g-NENs) are low, with high recurrence rates. This fact impacts patient prognosis and complicates postoperative management. Traditional prognostic models, including the Cox proportional hazards (CoxPH) model, have shown limited predictive power for postoperative survival in gastrointestinal neuroectodermal tumor patients. Machine learning methods offer a unique opportunity to analyze complex relationships within datasets, providing tools and methodologies to assess large volumes of high-dimensional, multimodal data generated by biological sciences. These methods show promise in predicting outcomes across various medical disciplines. In the context of g-NENs, utilizing machine learning to predict survival outcomes holds potential for personalized postoperative management strategies. This editorial reviews a study exploring the advantages and effectiveness of the random survival forest (RSF) model, using the lymph node ratio (LNR), in predicting disease-specific survival (DSS) in postoperative g-NEN patients stratified into low-risk and high-risk groups. The findings demonstrate that the RSF model, incorporating LNR, outperformed the CoxPH model in predicting DSS and constitutes an important step towards precision medicine.
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Affiliation(s)
- Hong-Niu Wang
- Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
| | - Jia-Hao An
- Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
| | - Liang Zong
- Department of Gastrointestinal Surgery, Changzhi People’s Hospital, Changzhi 046000, Shanxi Province, China
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Iacucci M, Santacroce G, Zammarchi I, Maeda Y, Del Amor R, Meseguer P, Kolawole BB, Chaudhari U, Di Sabatino A, Danese S, Mori Y, Grisan E, Naranjo V, Ghosh S. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol 2024; 9:758-772. [PMID: 38759661 DOI: 10.1016/s2468-1253(24)00053-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 05/19/2024]
Abstract
Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Pablo Meseguer
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain; Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | | | | | - Antonio Di Sabatino
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy; First Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele and University Vita-Salute San Raffaele, Milan, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Enrico Grisan
- School of Engineering, London South Bank University, London, UK
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
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Ravikulan A, Rostami K. Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine. World J Gastroenterol 2024; 30:424-428. [PMID: 38414588 PMCID: PMC10895597 DOI: 10.3748/wjg.v30.i5.424] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/19/2023] [Accepted: 01/12/2024] [Indexed: 01/31/2024] Open
Abstract
The high rate of early recurrence in hepatocellular carcinoma (HCC) post curative surgical intervention poses a substantial clinical hurdle, impacting patient outcomes and complicating postoperative management. The advent of machine learning provides a unique opportunity to harness vast datasets, identifying subtle patterns and factors that elude conventional prognostic methods. Machine learning models, equipped with the ability to analyse intricate relationships within datasets, have shown promise in predicting outcomes in various medical disciplines. In the context of HCC, the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies. This editorial comments on the study carried out exploring the merits and efficacy of random survival forests (RSF) in identifying significant risk factors for recurrence, stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models (CPH). In doing so, the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine.
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Affiliation(s)
- Abhimati Ravikulan
- Department of Gastroenterology, Palmerston North Hospital, Palmerston North 4442, New Zealand
| | - Kamran Rostami
- Department of Gastroenterology, Palmerston North Hospital, Palmerston North 4442, New Zealand
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Siddiqui F, Aslam D, Tanveer K, Soudy M. The Role of Artificial Intelligence and Machine Learning in Autoimmune Disorders. STUDIES IN COMPUTATIONAL INTELLIGENCE 2024:61-75. [DOI: 10.1007/978-981-99-9029-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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Christou CD, Tsoulfas G. Challenges involved in the application of artificial intelligence in gastroenterology: The race is on! World J Gastroenterol 2023; 29:6168-6178. [PMID: 38186861 PMCID: PMC10768398 DOI: 10.3748/wjg.v29.i48.6168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 11/06/2023] [Accepted: 12/18/2023] [Indexed: 12/27/2023] Open
Abstract
Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following applications: Models integrated into the clinical setting following real-time patient data flagging patients at high risk for developing a gastrointestinal disease, models employing non-invasive parameters that provide accurate diagnoses aiming to either replace, minimize, or refine the indications of endoscopy, models utilizing genomic data to diagnose various gastrointestinal diseases, computer-aided diagnosis systems facilitating the interpretation of endoscopy images, models to facilitate treatment allocation and predict the response to treatment, and finally, models in prognosis predicting complications, recurrence following treatment, and overall survival. Then, we elaborate on several challenges and how they may negatively impact the widespread application of AI in healthcare and gastroenterology. Specifically, we elaborate on concerns regarding accuracy, cost-effectiveness, cybersecurity, interpretability, oversight, and liability. While AI is unlikely to replace physicians, it will transform the skillset demanded by future physicians to practice. Thus, physicians are expected to engage with AI to avoid becoming obsolete.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
- Center for Research and Innovation in Solid Organ Transplantation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
- Center for Research and Innovation in Solid Organ Transplantation, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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8
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McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [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: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
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Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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Nashwan AJ, Abujaber AA, Choudry H. Embracing the future of physician-patient communication: GPT-4 in gastroenterology. GASTROENTEROLOGY & ENDOSCOPY 2023; 1:132-135. [DOI: 10.1016/j.gande.2023.07.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2025]
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Allaume P, Rabilloud N, Turlin B, Bardou-Jacquet E, Loréal O, Calderaro J, Khene ZE, Acosta O, De Crevoisier R, Rioux-Leclercq N, Pecot T, Kammerer-Jacquet SF. Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13101799. [PMID: 37238283 DOI: 10.3390/diagnostics13101799] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/04/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
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Affiliation(s)
- Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Edouard Bardou-Jacquet
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Department of Liver Diseases CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Olivier Loréal
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Department of Pathology Henri Mondor, 94000 Créteil, France
- INSERM U955, Team Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers, 94000 Créteil, France
| | - Zine-Eddine Khene
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Urology, CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Oscar Acosta
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Renaud De Crevoisier
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Radiotherapy, Centre Eugène Marquis, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Thierry Pecot
- Biosit Platform UAR 3480 CNRS US18 INSERM U955, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
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The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update. Cancers (Basel) 2023; 15:cancers15030708. [PMID: 36765671 PMCID: PMC9913834 DOI: 10.3390/cancers15030708] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review.
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Kamada Y, Nakamura T, Isobe S, Hosono K, Suama Y, Ohtakaki Y, Nauchi A, Yasuda N, Mitsuta S, Miura K, Yamamoto T, Hosono T, Yoshida A, Kawanishi I, Fukushima H, Kinoshita M, Umeda A, Kinoshita Y, Fukami K, Miyawaki T, Fujii H, Yoshida Y, Kawanaka M, Hyogo H, Morishita A, Hayashi H, Tobita H, Tomita K, Ikegami T, Takahashi H, Yoneda M, Jun DW, Sumida Y, Okanoue T, Nakajima A. SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT Forum. J Gastroenterol 2023; 58:79-97. [PMID: 36469127 PMCID: PMC9735102 DOI: 10.1007/s00535-022-01932-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/12/2022] [Indexed: 12/11/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Nonalcoholic steatohepatitis (NASH) is an advanced form of NAFLD can progress to liver cirrhosis and hepatocellular carcinoma (HCC). Recently, the prognosis of NAFLD/NASH has been reported to be dependent on liver fibrosis degree. Liver biopsy remains the gold standard, but it has several issues that must be addressed, including its invasiveness, cost, and inter-observer diagnosis variability. To solve these issues, a variety of noninvasive tests (NITs) have been in development for the assessment of NAFLD progression, including blood biomarkers and imaging methods, although the use of NITs varies around the world. The aim of the Japan NASH NIT (JANIT) Forum organized in 2020 is to advance the development of various NITs to assess disease severity and/or response to treatment in NAFLD patients from a scientific perspective through multi-stakeholder dialogue with open innovation, including clinicians with expertise in NAFLD/NASH, companies that develop medical devices and biomarkers, and professionals in the pharmaceutical industry. In addition to conventional NITs, artificial intelligence will soon be deployed in many areas of the NAFLD landscape. To discuss the characteristics of each NIT, we conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis in this study with the 36 JANIT Forum members (16 physicians and 20 company representatives). Based on this SWOT analysis, the JANIT Forum identified currently available NITs able to accurately select NAFLD patients at high risk of NASH for HCC surveillance/therapeutic intervention and evaluate the effectiveness of therapeutic interventions.
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Affiliation(s)
- Yoshihiro Kamada
- Department of Advanced Metabolic Hepatology, Osaka University Graduate School of Medicine, 1-7, Yamadaoka, Suita, Osaka, 565-0871 Japan
| | - Takahiro Nakamura
- Medicine Division, Nippon Boehringer Ingelheim Co., Ltd., 2-1-1, Osaki, Shinagawa-Ku, Tokyo, 141-6017 Japan
| | - Satoko Isobe
- FibroScan Division, Integral Corporation, 2-25-2, Kamiosaki, Shinagawa-Ku, Tokyo, 141-0021 Japan
| | - Kumiko Hosono
- Immunology, Hepatology & Dermatology Medical Franchise Dept., Medical Division, Novartis Pharma K.K., 1-23-1, Toranomon, Minato-Ku, Tokyo, 105-6333 Japan
| | - Yukiko Suama
- Medical Information Services, Institute of Immunology Co., Ltd., 1-1-10, Koraku, Bunkyo-Ku, Tokyo, 112-0004 Japan
| | - Yukie Ohtakaki
- Product Development 1St Group, Product Development Dept., Fujirebio Inc., 2-1-1, Nishishinjuku, Shinjuku-Ku, Tokyo, 163-0410 Japan
| | - Arihito Nauchi
- Academic Department, GE Healthcare Japan, 4-7-127, Asahigaoka, Hino, Tokyo, 191-8503 Japan
| | - Naoto Yasuda
- Ultrasound Business Area, Siemens Healthcare KK, 1-11-1, Osaki, Shinagawa-Ku, Tokyo, 141-8644 Japan
| | - Soh Mitsuta
- FibroScan Division, Integral Corporation, 2-25-2, Kamiosaki, Shinagawa-Ku, Tokyo, 141-0021 Japan
| | - Kouichi Miura
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, 3311-1, Yakushiji, Shimotsuke, Tochigi, 329-0498 Japan
| | - Takuma Yamamoto
- Cardiovascular and Diabetes, Product Marketing Department, Kowa Company, Ltd., 3-4-10, Nihonbashi Honcho, Chuo-Ku, Tokyo, 103-0023 Japan
| | - Tatsunori Hosono
- Clinical Development & Operations Japan, Nippon Boehringer Ingelheim Co., Ltd., 2-1-1, Osaki, Shinagawa-Ku, Tokyo, 141-6017 Japan
| | - Akihiro Yoshida
- Medical Affairs Department, Kowa Company, Ltd., 3-4-14, Nihonbashi Honcho, Chuo-Ku, Tokyo, 103-8433 Japan
| | - Ippei Kawanishi
- R&D Planning Department, EA Pharma Co., Ltd., 2-1-1, Irifune, Chuo-Ku, Tokyo, 104-0042 Japan
| | - Hideaki Fukushima
- Diagnostics Business Area, Siemens Healthcare Diagnostics KK, 1-11-1, Osaki, Shinagawa-Ku, Tokyo, 141-8673 Japan
| | - Masao Kinoshita
- Marketing Dep. H.U. Frontier, Inc., Shinjuku Mitsui Building, 2-1-1, Nishishinjuku, Shinjuku-Ku, Tokyo, 163-0408 Japan
| | - Atsushi Umeda
- Clinical Development Dept, EA Pharma Co., Ltd., 2-1-1, Irifune, Chuo-Ku, Tokyo, 104-0042 Japan
| | - Yuichi Kinoshita
- Global Drug Development Division, Novartis Pharma KK, 1-23-1, Toranomon, Minato-Ku, Tokyo, 105-6333 Japan
| | - Kana Fukami
- 2Nd Product Planning Dept, 2Nd Product Planning Division, Fujirebio Inc, 2-1-1, Nishishinjuku, Shinjuku-Ku, Tokyo, 163-0410 Japan
| | - Toshio Miyawaki
- Medical Information Services, Institute of Immunology Co., Ltd., 1-1-10, Koraku, Bunkyo-Ku, Tokyo, 112-0004 Japan
| | - Hideki Fujii
- Departments of Hepatology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-Machi, Abeno-Ku, Osaka, Osaka 545-8585 Japan
| | - Yuichi Yoshida
- Department of Gastroenterology and Hepatology, Suita Municipal Hospital, 5-7, Kishibe Shinmachi, Suita, Osaka 564-8567 Japan
| | - Miwa Kawanaka
- Department of General Internal Medicine2, Kawasaki Medical School, Kawasaki Medical Center, 2-6-1, Nakasange, Kita-Ku, Okayama, Okayama 700-8505 Japan
| | - Hideyuki Hyogo
- Department of Gastroenterology, JA Hiroshima Kouseiren General Hospital, 1-3-3, Jigozen, Hatsukaichi, Hiroshima 738-8503 Japan ,Hyogo Life Care Clinic Hiroshima, 6-34-1, Enkobashi-Cho, Minami-Ku, Hiroshima, Hiroshima 732-0823 Japan
| | - Asahiro Morishita
- Department of Gastroenterology and Neurology, Faculty of Medicine, Kagawa University, 1750-1, Oaza Ikenobe, Miki-Cho, Kita-Gun, Kagawa 761-0793 Japan
| | - Hideki Hayashi
- Department of Gastroenterology and Hepatology, Gifu Municipal Hospital, 7-1, Kashima-Cho, Gifu, Gifu 500-8513 Japan
| | - Hiroshi Tobita
- Division of Hepatology, Shimane University Hospital, 89-1, Enya-Cho, Izumo, Shimane 693-8501 Japan
| | - Kengo Tomita
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Defense Medical College, 3-2, Namiki, Tokorozawa, Saitama 359-8513 Japan
| | - Tadashi Ikegami
- Division of Gastroenterology and Hepatology, Tokyo Medical University Ibaraki Medical Center, 3-20-1, Chuo, Ami-Machi, Inashiki-Gun, Ibaraki, 300-0395 Japan
| | - Hirokazu Takahashi
- Liver Center, Faculty of Medicine, Saga University Hospital, Saga University, 5-1-1, Nabeshima, Saga, Saga 849-8501 Japan
| | - Masato Yoneda
- Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine Graduate School of Medicine, 3-9, Fukuura, Kanazawa-Ku, Yokohama, Kanagawa 236-0004 Japan
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul, 04763 Korea
| | - Yoshio Sumida
- Division of Hepatology and Pancreatology, Department of Internal Medicine, Aichi Medical University, 21 Yazako Karimata, Nagakute, Aichi, 480-1195, Japan.
| | - Takeshi Okanoue
- Department of Gastroenterology & Hepatology, Saiseikai Suita Hospital, Osaka, 1-2, Kawazono-Cho, Suita, Osaka 564-0013 Japan
| | - Atsushi Nakajima
- Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine Graduate School of Medicine, 3-9, Fukuura, Kanazawa-Ku, Yokohama, Kanagawa 236-0004 Japan
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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14
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Huang J, Zhao C, Zhang X, Zhao Q, Zhang Y, Chen L, Dai G. Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine. Front Pharmacol 2022; 13:1079566. [PMID: 36569318 PMCID: PMC9780394 DOI: 10.3389/fphar.2022.1079566] [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: 10/25/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence. Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio.sino.org/node), the Cancer Genome Atlas (TCGA), and our in-house hepatocellular carcinoma patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the "Gene set variation analysis" R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on hepatocellular carcinoma patient's clinical outcome were calculated. The utility of core immunosignals as a classifier for hepatocellular carcinoma tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the "oncoPredict" R package. Results: We identified nine genes comprising chemokines and ECM related to hepatitis B virus-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+ T cells bearing harmful factors (aCD4) during all stages of hepatitis B virus pathogenesis, which was also verified by our hepatocellular carcinoma data. Unexpectedly, we found that hepatitis B virus-hepatocellular carcinoma patients in the CLSThighaCD4high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage hepatocellular carcinoma via explainable machine learning. In addition, the 5-year long-term overall survival of hepatocellular carcinoma patients can be effectively classified by CLST/aCD4 based GeneSet-ResNet model. Subgroups defined by CLST and aCD4 were significantly involved in the sensitivity of hepatitis B virus-hepatocellular carcinoma patients to chemotherapy treatments. Conclusion: CLST and aCD4 are hepatitis B virus pathogenesis-relevant immunosignals that are highly involved in hepatitis B virus-induced inflammation, fibrosis, and hepatocellular carcinoma. Gene set variation analysis derived immunogenomic signatures enabled efficient diagnostic and prognostic model construction. The clinical application of CLST and aCD4 as indicators would be beneficial for the precision management of hepatocellular carcinoma.
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Affiliation(s)
- Jun Huang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Jun Huang, ; Liping Chen, ; Guifu Dai,
| | - Chunbei Zhao
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Xinhe Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Qiaohui Zhao
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Yanting Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Liping Chen
- Key Laboratory of Gastroenterology and Hepatology, State Key Laboratory for Oncogenes and Related Genes, Department of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China,Shanghai Public Health Clinical Center, Fudan University, Shanghai, China,*Correspondence: Jun Huang, ; Liping Chen, ; Guifu Dai,
| | - Guifu Dai
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Jun Huang, ; Liping Chen, ; Guifu Dai,
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15
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Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28:6230-6248. [PMID: 36504554 PMCID: PMC9730439 DOI: 10.3748/wjg.v28.i44.6230] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/07/2022] [Accepted: 11/16/2022] [Indexed: 11/25/2022] Open
Abstract
The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.
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Affiliation(s)
- J Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Marta Alonso-Bernáldez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | | | - Juan A Vargas-Nuñez
- Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
| | - Ana Ramírez de Molina
- Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Alberto Dávalos
- Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
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16
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Volovat SR, Augustin I, Zob D, Boboc D, Amurariti F, Volovat C, Stefanescu C, Stolniceanu CR, Ciocoiu M, Dumitras EA, Danciu M, Apostol DGC, Drug V, Shurbaji SA, Coca LG, Leon F, Iftene A, Herghelegiu PC. Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI. Cancers (Basel) 2022; 14:4834. [PMID: 36230757 PMCID: PMC9562853 DOI: 10.3390/cancers14194834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/18/2022] [Accepted: 09/29/2022] [Indexed: 12/09/2022] Open
Abstract
Colorectal cancer is a major cause of cancer-related death worldwide and is correlated with genetic and epigenetic alterations in the colonic epithelium. Genetic changes play a major role in the pathophysiology of colorectal cancer through the development of gene mutations, but recent research has shown an important role for epigenetic alterations. In this review, we try to describe the current knowledge about epigenetic alterations, including DNA methylation and histone modifications, as well as the role of non-coding RNAs as epigenetic regulators and the prognostic and predictive biomarkers in metastatic colorectal disease that can allow increases in the effectiveness of treatments. Additionally, the intestinal microbiota's composition can be an important biomarker for the response to strategies based on the immunotherapy of CRC. The identification of biomarkers in mCRC can be enhanced by developing artificial intelligence programs. We present the actual models that implement AI technology as a bridge connecting ncRNAs with tumors and conducted some experiments to improve the quality of the model used as well as the speed of the model that provides answers to users. In order to carry out this task, we implemented six algorithms: the naive Bayes classifier, the random forest classifier, the decision tree classifier, gradient boosted trees, logistic regression and SVM.
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Affiliation(s)
- Simona-Ruxandra Volovat
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Iolanda Augustin
- Department of Medical Oncology, AI.Trestioreanu Institute of Oncology, 022328 Bucharest, Romania
| | - Daniela Zob
- Department of Medical Oncology, AI.Trestioreanu Institute of Oncology, 022328 Bucharest, Romania
| | - Diana Boboc
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Florin Amurariti
- Department of Medical Oncology-Radiotherapy, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Constantin Volovat
- Department of Medical Oncology, “Euroclinic” Center of Oncology, 2 Vasile Conta Str., 700106 Iasi, Romania
| | - Cipriana Stefanescu
- Department of Biophysics and Medical Physics-Nuclear Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Cati Raluca Stolniceanu
- Department of Biophysics and Medical Physics-Nuclear Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
| | - Manuela Ciocoiu
- Department of Pathophysiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Eduard Alexandru Dumitras
- Department of Pathophysiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Anesthesiology and Intensive Care, Regional Institute of Oncology, 700115 Iasi, Romania
| | - Mihai Danciu
- Pathology Department, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | | | - Vasile Drug
- Department of Gastroenterology, “Grigore T. Popa” University of Medicine and Pharmacy, 16 University Str., 700115 Iasi, Romania
- Gastroenterology Clinic, Institute of Gastroenterology and Hepatology, ‘St. Spiridon’ Clinical Hospital, 700115 Iasi, Romania
| | - Sinziana Al Shurbaji
- Gastroenterology Clinic, Institute of Gastroenterology and Hepatology, ‘St. Spiridon’ Clinical Hospital, 700115 Iasi, Romania
| | - Lucia-Georgiana Coca
- Faculty of Computer Science, Alexandru Ioan Cuza University, 700115 Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 700115 Iasi, Romania
| | - Adrian Iftene
- Faculty of Computer Science, Alexandru Ioan Cuza University, 700115 Iasi, Romania
| | - Paul-Corneliu Herghelegiu
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 700115 Iasi, Romania
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17
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Jergens AE, Heilmann RM. Canine chronic enteropathy—Current state-of-the-art and emerging concepts. Front Vet Sci 2022; 9:923013. [PMID: 36213409 PMCID: PMC9534534 DOI: 10.3389/fvets.2022.923013] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Over the last decade, chronic inflammatory enteropathies (CIE) in dogs have received great attention in the basic and clinical research arena. The 2010 ACVIM Consensus Statement, including guidelines for the diagnostic criteria for canine and feline CIE, was an important milestone to a more standardized approach to patients suspected of a CIE diagnosis. Great strides have been made since understanding the pathogenesis and classification of CIE in dogs, and novel diagnostic and treatment options have evolved. New concepts in the microbiome-host-interaction, metabolic pathways, crosstalk within the mucosal immune system, and extension to the gut-brain axis have emerged. Novel diagnostics have been developed, the clinical utility of which remains to be critically evaluated in the next coming years. New directions are also expected to lead to a larger spectrum of treatment options tailored to the individual patient. This review offers insights into emerging concepts and future directions proposed for further CIE research in dogs for the next decade to come.
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Affiliation(s)
- Albert E. Jergens
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Iowa State University, Ames, IA, United States
- *Correspondence: Albert E. Jergens
| | - Romy M. Heilmann
- Department for Small Animals, College of Veterinary Medicine, University of Leipzig, Leipzig, SN, Germany
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18
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Lewis JH. Digitizing DILI: Who can? RUCAM? RECAM? Hepatology 2022; 76:3-5. [PMID: 34990036 DOI: 10.1002/hep.32312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 12/15/2022]
Affiliation(s)
- James H Lewis
- Department of Medicine, Georgetown University Hospital, Washington, District of Columbia, USA
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19
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Tonini V, Vigutto G, Donati R. Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligence. Artif Intell Gastroenterol 2022; 3:28-35. [DOI: 10.35712/aig.v3.i2.28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the most common neoplasia with an high risk to metastatic spread. Improving medical and surgical treatment is moving along with improving the precision of diagnosis and patient's assessment, the latter two aided more and more with the use of artificial intelligence (AI). The management of colorectal liver metastasis is multidisciplinary, and surgery is the main option. After the diagnosis, a surgical assessment of the patient is fundamental. Reaching a R0 resection with a proper remnant liver volume can be done using new techniques involving also artificial intelligence. Considering the recent application of artificial intelligence as a valid substitute for liver biopsy in chronic liver diseases, several authors tried to apply similar techniques to pre-operative imaging of liver metastasis. Radiomics showed good results in identifying structural changes in a unhealthy liver and in evaluating the prognosis after a liver resection. Recently deep learning has been successfully applied in estimating the remnant liver volume before surgery. Moreover AI techniques can help surgeons to perform an early diagnosis of neoplastic relapse or a better differentiation between a colorectal metastasis and a benign lesion. AI could be applied also in the histopathological diagnostic tool. Although AI implementation is still partially automatized, it appears faster and more precise than the usual diagnostic tools and, in the short future, could become the new gold standard in liver surgery.
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Affiliation(s)
- Valeria Tonini
- Department of Medical and Surgical Sciences, Sant' Orsola Hospital University of Bologna, Bologna 40138, Italy
| | - Gabriele Vigutto
- Department of Medical and Surgical Sciences, St Orsola Hospital, University of Bologna, Bologna 40138, Italy
| | - Riccardo Donati
- Department of Electrical, Electronic and Information Engineering ”Guglielmo Marconi” (DEI), University of Bologna, Bologna 40138, Italy
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20
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Mucenic M, de Mello Brandão AB, Marroni CA. Artificial intelligence and human liver allocation: Potential benefits and ethical implications. Artif Intell Gastroenterol 2022; 3:21-27. [DOI: 10.35712/aig.v3.i1.21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [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
Abstract
Since its implementation almost two decades ago, the urgency allocation policy has improved the survival of patients on the waiting list for liver transplantation worldwide. The Model for End-Stage Liver Disease score is widely used to predict waiting list mortality. Due to some limitations related to its use, there is an active investigation to develop other prognostic scores. Liver allocation (LA) entails complex decision-making, and grafts are occasionally not directed to the recipients who are more likely to survive. Prognostic scores have, thus far, failed to predict post-operatory survival. Furthermore, the increasing use of marginal donors is associated with worse outcomes. Adequate donor-recipient pairing could help avoid retransplantation or futile procedures and reduce postoperative complications, mortality, hospitalization time, and costs. Artificial intelligence has applications in several medical fields. Machine learning algorithms (MLAs) use large amounts of data to detect unforeseen patterns and complex interactions between variables. Artificial neural networks and decision trees were the most common forms of MLA tested on LA. Some researchers have shown them to be superior for predicting waiting list mortality and graft failure than conventional statistical methods. These promising techniques are increasingly being considered for implementation.
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Affiliation(s)
- Marcos Mucenic
- Liver Transplant Adult Group, Irmandade da Santa Casa de Misericórdia de Porto Alegre, Porto Alegre 90020-090, RS, Brazil
| | | | - Claudio Augusto Marroni
- Hepatology, Universidade Federal de Ciencias da Saude de Porto Alegre, Porto Alegre 90050-170, RS, Brazil
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21
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Christou CD, Athanasiadou EC, Tooulias AI, Tzamalis A, Tsoulfas G. The process of estimating the cost of surgery: Providing a practical framework for surgeons. Int J Health Plann Manage 2022; 37:1926-1940. [PMID: 35191067 DOI: 10.1002/hpm.3431] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 10/25/2021] [Accepted: 01/21/2022] [Indexed: 02/05/2023] Open
Abstract
Over the last decades, health care costs have been increasing at an alarming, exponential rate which is considered unsustainable. Surgical care utilizes one-third of health care costs. Estimating, evaluating, and understanding the cost of surgery is a vital step towards cost management and reduction. Current cost estimation studies and cost-effectiveness studies have vast disparities in their methodology, with published costs of Operating Room varying from as low as $7 and as high as $113 per minute. Costs in surgery are distinguished as direct and indirect. Allocation of direct costs involves identification, measurement, and valuation processes. Allocation of indirect costs involves the allocation of capital and overhead costs and of indirect department costs. Annualised capital costs and overhead hospital costs are then allocated to surgery by either the cost-centre allocation or the activity-based allocation frameworks. Indirect department costs are allocated to a specific surgery by weighted service allocation or hourly rate allocation or inpatient day allocation, or marginal markup allocation. The growing societal, financial and political pressure for cost reduction has brought cost analysis to the forefront of healthcare discussions. Thus, we believe that almost every single surgeon will eventually enter the field of healthcare economics by necessity. This review aims to provide surgeons with a practical framework for engaging in cost estimation studies.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleni C Athanasiadou
- Surgical Oncology Department, Theageneio Anticancer Hospital of Thessaloniki, Thessaloniki, Greece
| | - Andreas I Tooulias
- First General Surgery Department, Papageorgiou General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Argyrios Tzamalis
- Second Department of Ophthalmology, Papageorgiou General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
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22
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Czako Z, Surdea-Blaga T, Sebestyen G, Hangan A, Dumitrascu DL, David L, Chiarioni G, Savarino E, Popa SL. Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning. SENSORS 2021; 22:s22010253. [PMID: 35009794 PMCID: PMC8749817 DOI: 10.3390/s22010253] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/24/2021] [Accepted: 12/27/2021] [Indexed: 12/29/2022]
Abstract
High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.
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Affiliation(s)
- Zoltan Czako
- Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania; (Z.C.); (G.S.); (A.H.)
| | - Teodora Surdea-Blaga
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania; (D.L.D.); (L.D.); (S.L.P.)
- Correspondence:
| | - Gheorghe Sebestyen
- Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania; (Z.C.); (G.S.); (A.H.)
| | - Anca Hangan
- Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania; (Z.C.); (G.S.); (A.H.)
| | - Dan Lucian Dumitrascu
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania; (D.L.D.); (L.D.); (S.L.P.)
| | - Liliana David
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania; (D.L.D.); (L.D.); (S.L.P.)
| | - Giuseppe Chiarioni
- Division of Gastroenterology, University of Verona, AOUI Verona, 37134 Verona, Italy;
| | - Edoardo Savarino
- Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35100 Padova, Italy;
| | - Stefan Lucian Popa
- Second Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400027 Cluj-Napoca, Romania; (D.L.D.); (L.D.); (S.L.P.)
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Popa SL, Ismaiel A. Artificial intelligence applications in gastroenterology: steps ahead. Med Pharm Rep 2021; 94:S56-S59. [PMID: 38912404 PMCID: PMC11188025 DOI: 10.15386/mpr-2513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
Artificial intelligence (AI) applications are used in gastroenterology for automatic imaging diagnostic methods such as ultrasonography, computer tomography, magnetic resonance imaging, but also in endoscopy, capsule endoscopy and biopsy followed by automatic digital pathology evaluation. The accuracy of AI-based systems is superior to human expertise. Furthermore, in reality, a very small percentage of the patients are being investigated by a human expert in endoscopy, so implementing AI in this investigation would only increase the diagnostic accuracy. The existence of an unimaginable number of digital images and different types of medical information made possible the analysis and training of convolutional neural network (CNN), which consists of multilayers of artificial neural networks (ANN) with step-by-step minimal processing, creating a fundamental resource for any AI-based system to learn by itself how to automatically perform medical tasks, which were performed only by human experts in the past. The main objectives for AI applications used in gastroenterology are to improve the medical procedures with enhanced precision, to reduce the number of medical errors and to perform repetitive tasks.
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Affiliation(s)
- Stefan L Popa
- 2 Medical Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Abdulrahman Ismaiel
- 2 Medical Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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