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Chongo G, Soldera J. Use of machine learning models for the prognostication of liver transplantation: A systematic review. World J Transplant 2024; 14:88891. [PMID: 38576762 PMCID: PMC10989468 DOI: 10.5500/wjt.v14.i1.88891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 03/15/2024] Open
Abstract
BACKGROUND Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. AIM To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. RESULTS Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.
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Affiliation(s)
- Gidion Chongo
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
| | - Jonathan Soldera
- Department of Gastroenterology, University of South Wales, Cardiff CF37 1DL, United Kingdom
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Alonso A, Siracuse JJ. Protecting patient safety and privacy in the era of artificial intelligence. Semin Vasc Surg 2023; 36:426-429. [PMID: 37863615 DOI: 10.1053/j.semvascsurg.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 06/14/2023] [Accepted: 06/20/2023] [Indexed: 10/22/2023]
Abstract
The promise of artificial intelligence (AI) in health care has propelled a significant uptrend in the number of clinical trials in AI and global market spending in this novel technology. In vascular surgery, this technology has the ability to diagnose disease, predict disease outcomes, and assist with image-guided surgery. As we enter an era of rapid change, it is critical to evaluate the ethical concerns of AI, particularly as it may impact patient safety and privacy. This is particularly important to discuss in the early stages of AI, as technology frequently outpaces the policies and ethical guidelines regulating it. Issues at the forefront include patient privacy and confidentiality, protection of patient autonomy and informed consent, accuracy and applicability of this technology, and propagation of health care disparities. Vascular surgeons should be equipped to work with AI, as well as discuss its novel risks to patient safety and privacy.
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Affiliation(s)
- Andrea Alonso
- Division of Vascular and Endovascular Surgery, Department of Surgery, Boston Medical Center, Chobanian and Avedisian School of Medicine, Boston University, 85 E. Concord St, Boston, MA 02118
| | - Jeffrey J Siracuse
- Division of Vascular and Endovascular Surgery, Department of Surgery, Boston Medical Center, Chobanian and Avedisian School of Medicine, Boston University, 85 E. Concord St, Boston, MA 02118.
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3
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Wang N, Zhang Y, Wang W, Ye Z, Chen H, Hu G, Ouyang D. How can machine learning and multiscale modeling benefit ocular drug development? Adv Drug Deliv Rev 2023; 196:114772. [PMID: 36906232 DOI: 10.1016/j.addr.2023.114772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/06/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023]
Abstract
The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.
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Affiliation(s)
- Nannan Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Yunsen Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Wei Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Zhuyifan Ye
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China
| | - Hongyu Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Guanghui Hu
- Faculty of Science and Technology (FST), University of Macau, Macau, China
| | - Defang Ouyang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS), University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences (FHS), University of Macau, Macau, China.
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Koleth G, Emmanue J, Spadaccini M, Mascagni P, Khalaf K, Mori Y, Antonelli G, Maselli R, Carrara S, Galtieri PA, Pellegatta G, Fugazza A, Anderloni A, Selvaggio C, Bretthauer M, Aghemo A, Spinelli A, Savevski V, Sharma P, Hassan C, Repici A, Hassan C, Repici A. Artificial intelligence in gastroenterology: Where are we heading? Endosc Int Open 2022; 10:E1474-E1480. [PMID: 36397868 PMCID: PMC9666060 DOI: 10.1055/a-1907-6569] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 07/20/2022] [Indexed: 10/17/2022] Open
Abstract
Background and study aims Artificial intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in gastroenterology while predicting its future applications. Methods All studies registered on Clinicaltrials.gov up to November 2021 were analyzed. The studies included used AI in gastrointestinal endoscopy, inflammatory bowel disease (IBD), hepatology, and pancreatobiliary diseases. Data regarding the study field, methodology, endpoints, and publication status were retrieved, pooled, and analyzed to observe underlying temporal and geographical trends. Results Of the 103 study entries retrieved according to our inclusion/exclusion criteria, 76 (74 %) were based on AI application to gastrointestinal endoscopy, mainly for detection and characterization of colorectal neoplasia (52/103, 50 %). Image analysis was also more frequently reported than data analysis for pancreaticobiliary (six of 10 [60 %]), liver diseases (eight of nine [89 %]), and IBD (six of eight [75 %]). Overall, 48 of 103 study entries (47 %) were interventional and 55 (53 %) observational. In 2018, one of eight studies (12.5 %) were interventional, while in 2021, 21 of 34 (61.8 %) were interventional, with an inverse ratio between observational and interventional studies during the study period. The majority of the studies were planned as single-center (74 of 103 [72 %]) and more were in Asia (45 of 103 [44 %]) and Europe (44 of 103 [43 %]). Conclusions AI implementation in gastroenterology is dominated by computer-aided detection and characterization of colorectal neoplasia. The timeframe for translational research is characterized by a swift conversion of observational into interventional studies.
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Affiliation(s)
- Glenn Koleth
- Hospital Selayang, Department of Gastroenterology and Hepatology, Selangor, Malaysia
| | - James Emmanue
- Hospital Selayang, Department of Gastroenterology and Hepatology, Selangor, Malaysia,Queen Elizabeth Hospital, Department of Gastroenterology and Hepatology, Sabah, Malaysia
| | - Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Pietro Mascagni
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy,Institute of Image-Guided Surgery, IHU-Strasbourg, France
| | - Kareem Khalaf
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway,Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Giulio Antonelli
- "Sapienza" University of Rome, Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Rome, Italy,Ospedale dei Castelli Hospital, Gastroenterology and Digestive Endoscopy Unit, Ariccia, Rome, Italy
| | - Roberta Maselli
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Silvia Carrara
- Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | | | - Gaia Pellegatta
- Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Fugazza
- Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Andrea Anderloni
- Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Carmelo Selvaggio
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy
| | - Michael Bretthauer
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Alessio Aghemo
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Humanitas Research Hospital – IRCCS, Internal Medicine and Hepatology Unit, Rozzano, Italy
| | - Antonino Spinelli
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Division of Colon and Rectal Surgery, Humanitas Clinical and Research Hospital IRCCS, Rozzano, Milan, Italy
| | - Victor Savevski
- Humanitas Clinical and Research Center – IRCCS, Artificial Intelligence Research, Rozzano, Italy
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology and Hepatology, Kansas City, United States
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy,Humanitas Clinical and Research Center – IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Cesare Hassan
- Digestive Endoscopy Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Digestive Endoscopy Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
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Wang A, Xiu X, Liu S, Qian Q, Wu S. Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13691. [PMID: 36294269 PMCID: PMC9602501 DOI: 10.3390/ijerph192013691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/13/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI's development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI's actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation.
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Affiliation(s)
| | | | | | | | - Sizhu Wu
- Correspondence: ; Tel.: +86-10-5232-8760
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Spanos K, Giannoukas AD, Kouvelos G, Tsougos I, Mavroforou A. Artificial Intelligence application in Vascular Diseases. J Vasc Surg 2022; 76:615-619. [PMID: 35661694 DOI: 10.1016/j.jvs.2022.03.895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/11/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Konstantinos Spanos
- Department of Vascular Surgery, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - Athanasios D Giannoukas
- Department of Vascular Surgery, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - George Kouvelos
- Department of Vascular Surgery, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - Ioannis Tsougos
- Department of Medical Physics and Informatics, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece.
| | - Anna Mavroforou
- Deontology and Bioethics Lab, Faculty of Nursing, School of Health Sciences, University of Thessaly, Larissa, Greece.
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Harris S, Bonnici T, Keen T, Lilaonitkul W, White MJ, Swanepoel N. Clinical deployment environments: Five pillars of translational machine learning for health. Front Digit Health 2022; 4:939292. [PMID: 36060542 PMCID: PMC9437594 DOI: 10.3389/fdgth.2022.939292] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/25/2022] [Indexed: 01/14/2023] Open
Abstract
Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from "real-world" data to "real-world" development.
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Affiliation(s)
- Steve Harris
- Institute of Health Informatics, University College London, London, United Kingdom
- Department of Critical Care, University College London Hospital, London, United Kingdom
- Correspondence: Steve Harris
| | - Tim Bonnici
- Institute of Health Informatics, University College London, London, United Kingdom
- Department of Critical Care, University College London Hospital, London, United Kingdom
| | - Thomas Keen
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Watjana Lilaonitkul
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Mark J. White
- Digital Healthcare, University College London Hospital, London, United Kingdom
| | - Nel Swanepoel
- Centre for Advanced Research Computing, University College London, London, United Kingdom
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