Artificial Intelligence in Gastroenterology最新文献

{"title":"Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology","authors":"Muhammed Mubarak, Rahma Rashid, Fnu Sapna, Shaheera Shakeel","doi":"10.35712/aig.v5.i2.91550","DOIUrl":"https://doi.org/10.35712/aig.v5.i2.91550","url":null,"abstract":"Digital pathology (DP) and its subsidiaries including artificial intelligence (AI) are rapidly making inroads into the area of diagnostic anatomic pathology (AP) including gastrointestinal (GI) pathology. It is poised to revolutionize the field of diagnostic AP. Historically, AP has been slow to adopt digital technology, but this is changing rapidly, with many centers worldwide transitioning to DP. Coupled with advanced techniques of AI such as deep learning and machine learning, DP is likely to transform histopathology from a subjective field to an objective, efficient, and transparent discipline. AI is increasingly integrated into GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care. Specifically, AI in GI pathology enhances diagnostic accuracy, streamlines workflows, provides predictive insights, integrates multimodal data, supports research, and aids in education and training, ultimately improving patient care and outcomes. This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology. The main aim was to provide updates and create awareness among the pathology community.","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of artificial intelligence in Barrett’s esophagus","authors":"C. N. Tee, Rajesh Ravi, T. Ang, J. W. Li","doi":"10.35712/aig.v4.i2.28","DOIUrl":"https://doi.org/10.35712/aig.v4.i2.28","url":null,"abstract":"The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130662088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug-induced liver injury and COVID-19: Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice","authors":"Gabriela X. Ortiz, Ana Helena Dias Pereira dos Santos Ulbrich, Gabriele Lenhart, Henrique Dias Pereira dos Santos, Karin Hepp Schwambach, Matheus William Becker, C. Blatt","doi":"10.35712/aig.v4.i2.36","DOIUrl":"https://doi.org/10.35712/aig.v4.i2.36","url":null,"abstract":"The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"155 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125905456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori: A study of a tertiary care hospital in Pakistan","authors":"Shahid Aziz, Simone König, M. Umer, T. Akhter, Shafqat Iqbal, Maryum Ibrar, Tofeeq Ur-Rehman, T. Ahmad, A. Hanafiah, R. Zahra, F. Rasheed","doi":"10.35712/aig.v4.i1.10","DOIUrl":"https://doi.org/10.35712/aig.v4.i1.10","url":null,"abstract":"","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114222414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data and variceal rebleeding prediction in cirrhosis patients","authors":"Q. Yuan, Wen-long Zhao, Bo Qin","doi":"10.35712/aig.v4.i1.1","DOIUrl":"https://doi.org/10.35712/aig.v4.i1.1","url":null,"abstract":"","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124819552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology","authors":"Aysen Yavuz, Anıl Alpsoy, E. Gedik, Mennan Yigitcan Celik, C. Başsorgun, B. Unal, G. Elpek","doi":"10.35712/aig.v3.i5.142","DOIUrl":"https://doi.org/10.35712/aig.v3.i5.142","url":null,"abstract":"","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127558816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in gastroenterology: A narrative review","authors":"J. Galati, Robert J. Duve, Matthew O'Mara, S. Gross","doi":"10.35712/aig.v3.i5.117","DOIUrl":"https://doi.org/10.35712/aig.v3.i5.117","url":null,"abstract":"","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"484 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126263775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease","authors":"Y. Kusano, K. Funada, Mayumi Yamaguchi, Miwa Sugawara, Masaya Tamano","doi":"10.35712/aig.v3.i4.105","DOIUrl":"https://doi.org/10.35712/aig.v3.i4.105","url":null,"abstract":"Name of journal: Artificial Intelligence in Gastroenterology Manuscript NO: 78338 Title: Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease Provenance and peer review: Unsolicited Manuscript; Externally peer reviewed Peer-review model: Single blind Reviewer’s code: 03093768 Position: Editorial Board Academic degree: MD Professional title: Associate Professor, Chief Doctor, Doctor, Surgeon, Surgical Oncologist Reviewer’s Country/Territory: China Author’s Country/Territory: Japan Manuscript submission date: 2022-06-22 Reviewer chosen by: AI Technique Reviewer accepted review: 2022-06-22 13:26 Reviewer performed review: 2022-06-26 07:00 Review time: 3 Days and 17 Hours","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127900184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma","authors":"Rajesh Kumar Mokhria, Jasbir Singh","doi":"10.35712/aig.v3.i4.96","DOIUrl":"https://doi.org/10.35712/aig.v3.i4.96","url":null,"abstract":"Artificial intelligence (AI) evolved many years ago, but it gained much advance-ment in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115414371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases","authors":"R. Carteri, M. Grellert, Daniela Luisa Borba, C. Marroni, S. Fernandes","doi":"10.35712/aig.v3.i3.80","DOIUrl":"https://doi.org/10.35712/aig.v3.i3.80","url":null,"abstract":"The prevalence of nonalcoholic fatty liver disease (NAFLD) is an important public health concern. Early diagnosis of NAFLD and potential progression to nonalcoholic steatohepatitis (NASH), could reduce the further advance of the disease, and improve patient outcomes. Aiming to support patient diagnostic and predict specific outcomes, the interest in artificial intelligence (AI) methods in hepatology has dramatically increased, especially with the application of less-invasive biomarkers. In this review, our objective was twofold: Firstly, we presented the most frequent blood biomarkers in NAFLD and NASH and secondly, we reviewed recent literature regarding the use of machine learning (ML) methods to predict NAFLD and NASH in large cohorts. Strikingly, these studies provide insights into ML application in NAFLD patients' prognostics and ranked blood biomarkers are able to provide a recognizable signature allowing cost-effective NAFLD prediction and also differentiating NASH patients. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science. Core Tip: The ability of machine learning approaches to process multiple variables, map linear and nonlinear interactions, ranking the most important features, in addition to the capability of building accurate prediction models, sets a future direction to its application in complex diseases such as nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science. blood Abstract While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a trans-formation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field. Core Tip: Artificial intelligence (AI) aided by multiple imaging modalities is accurate and effective for diagnosis and characterization of biliary masses. The advancement and incorporation of imaging into artificial intelligence will help to decrease delay in diagnosis of cholangiocarcinoma and","PeriodicalId":359415,"journal":{"name":"Artificial Intelligence in Gastroenterology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129625767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}