Vlad Dumitru Brata, Victor Incze, Abdulrahman Ismaiel, Daria Claudia Turtoi, Simona Grad, Raluca Popovici, Traian Adrian Duse, Teodora Surdea-Blaga, Alexandru Marius Padureanu, Liliana David, Miruna Oana Dita, Corina Alexandrina Baldea, Stefan Lucian Popa
{"title":"Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.","authors":"Vlad Dumitru Brata, Victor Incze, Abdulrahman Ismaiel, Daria Claudia Turtoi, Simona Grad, Raluca Popovici, Traian Adrian Duse, Teodora Surdea-Blaga, Alexandru Marius Padureanu, Liliana David, Miruna Oana Dita, Corina Alexandrina Baldea, Stefan Lucian Popa","doi":"10.3390/jpm14091012","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Esophageal varices, dilated submucosal veins in the lower esophagus, are commonly associated with portal hypertension, particularly due to liver cirrhosis. The high morbidity and mortality linked to variceal hemorrhage underscore the need for accurate diagnosis and effective management. The traditional method of assessing esophageal varices is esophagogastroduodenoscopy (EGD), which, despite its diagnostic and therapeutic capabilities, presents limitations such as interobserver variability and invasiveness. This review aims to explore the role of artificial intelligence (AI) in enhancing the management of esophageal varices, focusing on its applications in diagnosis, risk stratification, and treatment optimization.</p><p><strong>Methods: </strong>This systematic review focuses on the capabilities of AI algorithms to analyze clinical scores, laboratory data, endoscopic images, and imaging modalities like CT scans.</p><p><strong>Results: </strong>AI-based systems, particularly machine learning (ML) and deep learning (DL) algorithms, have demonstrated the ability to improve risk stratification and diagnosis of esophageal varices, analyzing vast amounts of data, identifying patterns, and providing individualized recommendations. However, despite these advancements, clinical scores based on laboratory data still show low specificity for esophageal varices, often requiring confirmatory endoscopic or imaging studies.</p><p><strong>Conclusions: </strong>AI integration in managing esophageal varices offers significant potential for advancing diagnosis, risk assessment, and treatment strategies. While promising, AI systems should complement rather than replace traditional methods, ensuring comprehensive patient evaluation. Further research is needed to refine these technologies and validate their efficacy in clinical practice.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11433421/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Personalized Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/jpm14091012","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Esophageal varices, dilated submucosal veins in the lower esophagus, are commonly associated with portal hypertension, particularly due to liver cirrhosis. The high morbidity and mortality linked to variceal hemorrhage underscore the need for accurate diagnosis and effective management. The traditional method of assessing esophageal varices is esophagogastroduodenoscopy (EGD), which, despite its diagnostic and therapeutic capabilities, presents limitations such as interobserver variability and invasiveness. This review aims to explore the role of artificial intelligence (AI) in enhancing the management of esophageal varices, focusing on its applications in diagnosis, risk stratification, and treatment optimization.
Methods: This systematic review focuses on the capabilities of AI algorithms to analyze clinical scores, laboratory data, endoscopic images, and imaging modalities like CT scans.
Results: AI-based systems, particularly machine learning (ML) and deep learning (DL) algorithms, have demonstrated the ability to improve risk stratification and diagnosis of esophageal varices, analyzing vast amounts of data, identifying patterns, and providing individualized recommendations. However, despite these advancements, clinical scores based on laboratory data still show low specificity for esophageal varices, often requiring confirmatory endoscopic or imaging studies.
Conclusions: AI integration in managing esophageal varices offers significant potential for advancing diagnosis, risk assessment, and treatment strategies. While promising, AI systems should complement rather than replace traditional methods, ensuring comprehensive patient evaluation. Further research is needed to refine these technologies and validate their efficacy in clinical practice.
期刊介绍:
Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.