Diagnostic accuracy of artificial intelligence for detecting Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and gastrointestinal luminal pathologies: A systematic review and meta-analysis meta-analysis

Background: Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms

[1]  S. Gross,et al.  DEEP LEARNING COMPUTER-AIDED POLYP DETECTION REDUCES ADENOMA MISS RATE: A U.S. MULTI-CENTER RANDOMIZED TANDEM COLONOSCOPY STUDY (CADeT-CS Trial). , 2021, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[2]  Zahra Hoodbhoy,et al.  Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis , 2021, Frontiers in Artificial Intelligence.

[3]  Jai K Das,et al.  Machine Learning for Child and Adolescent Health: A Systematic Review , 2020, Pediatrics.

[4]  Chengwei Tang,et al.  Detection of multiple lesions of gastrointestinal tract for endoscopy using artificial intelligence model: a pilot study , 2020, Surgical Endoscopy.

[5]  Limsoon Wong,et al.  Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability , 2020, Patterns.

[6]  Theodoros P. Zanos,et al.  External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19 , 2020, Nature Machine Intelligence.

[7]  Kang Ryoung Park,et al.  Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation , 2020, Journal of medical Internet research.

[8]  Satish K. Singh,et al.  Real-time artificial intelligence-based histological classification of colorectal polyps with augmented visualization. , 2020, Gastrointestinal endoscopy.

[9]  E. Mayo-Wilson,et al.  The PRISMA 2020 statement: an updated guideline for reporting systematic reviews , 2020, BMJ.

[10]  Ji Li,et al.  An improved deep learning approach and its applications on colonic polyp images detection , 2020, BMC Medical Imaging.

[11]  M. Vieth,et al.  A computer-assisted algorithm for narrow-band-imaging-based tissue characterization in Barrett's esophagus. , 2020, Gastrointestinal endoscopy.

[12]  T. Xia,et al.  Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy. , 2020, Gastrointestinal endoscopy.

[13]  Xiongxiong He,et al.  WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding , 2020, International Journal of Computer Assisted Radiology and Surgery.

[14]  Carmen C. Y. Poon,et al.  AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices , 2020, npj Digital Medicine.

[15]  B. Wahl,et al.  Artificial intelligence and the future of global health , 2020, The Lancet.

[16]  C. Van Steenkiste,et al.  Current challenges and future needs of clinical and endoscopic training in gastroenterology: a European survey , 2020, Endoscopy International Open.

[17]  Tsuyoshi Ozawa,et al.  Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks , 2020, Therapeutic advances in gastroenterology.

[18]  J. S. Kim,et al.  Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations. , 2020, Gastroenterology.

[19]  E. Schoon,et al.  Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video). , 2020, Gastrointestinal endoscopy.

[20]  Eun Mi Song,et al.  Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model , 2020, Scientific Reports.

[21]  Ilangko Balasingham,et al.  Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video , 2020, IEEE Journal of Biomedical and Health Informatics.

[22]  Xu Zhang,et al.  High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience , 2019, Clinical and translational gastroenterology.

[23]  Liang Huang,et al.  Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction , 2019, Comput. Methods Programs Biomed..

[24]  E. Klang,et al.  Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy. , 2019, Gastrointestinal endoscopy.

[25]  Edward J. Ciaccio,et al.  Celiac Disease Detection From Videocapsule Endoscopy Images Using Strip Principal Component Analysis , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  Yuxiang Xing,et al.  A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks , 2019, Physics in medicine and biology.

[27]  Andrew Q. Ninh,et al.  Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds. , 2019, The American journal of gastroenterology.

[28]  Hui Tian,et al.  Gastroscopic Panoramic View: Application to Automatic Polyps Detection under Gastroscopy , 2019, Comput. Math. Methods Medicine.

[29]  Yuxiang Xing,et al.  Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization , 2019, Comput. Math. Methods Medicine.

[30]  Michelle Viscaino,et al.  Machine learning for computer-aided polyp detection using wavelets and content-based image , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[31]  T. Mahmood,et al.  Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis , 2019, Journal of clinical medicine.

[32]  Nader Karimi,et al.  Deep Neural Network based Polyp Segmentation in Colonoscopy Images using a Combination of Color Spaces , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[33]  Donald E. Brown,et al.  Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children , 2019, JAMA network open.

[34]  Huilong Duan,et al.  Real-time gastric polyp detection using convolutional neural networks , 2019, PloS one.

[35]  Tsuyoshi Ozawa,et al.  Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. , 2019, Gastrointestinal endoscopy.

[36]  Isabel N. Figueiredo,et al.  Polyp detection with computer-aided diagnosis in white light colonoscopy: comparison of three different methods , 2019, Endoscopy International Open.

[37]  Ilangko Balasingham,et al.  Automatic polyp frame screening using patch based combined feature and dictionary learning , 2018, Comput. Medical Imaging Graph..

[38]  D. Mateus,et al.  Optical classification of neoplastic colorectal polyps – a computer-assisted approach (the COACH study) , 2018, Scandinavian journal of gastroenterology.

[39]  K. Mori,et al.  Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy , 2018, Annals of Internal Medicine.

[40]  H. Kong,et al.  A novel summary report of colonoscopy: timeline visualization providing meaningful colonoscopy video information , 2018, International Journal of Colorectal Disease.

[41]  E. Schoon,et al.  Computer-aided detection of early Barrett's neoplasia using volumetric laser endomicroscopy. , 2017, Gastrointestinal endoscopy.

[42]  Henry Horng-Shing Lu,et al.  Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis. , 2017, Gastroenterology.

[43]  Ilangko Balasingham,et al.  Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[44]  Yixuan Yuan,et al.  Deep learning for polyp recognition in wireless capsule endoscopy images , 2017, Medical physics.

[45]  Leontios J Hadjileontiadis,et al.  Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images , 2016, World journal of gastroenterology.

[46]  G. Fernández-Esparrach,et al.  Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps , 2016, Endoscopy.

[47]  Ilangko Balasingham,et al.  Edge density based automatic detection of inflammation in colonoscopy videos , 2016, Comput. Biol. Medicine.

[48]  Nima Tajbakhsh,et al.  Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information , 2016, IEEE Transactions on Medical Imaging.

[49]  Nima Tajbakhsh,et al.  A Comprehensive Computer-Aided Polyp Detection System for Colonoscopy Videos , 2015, IPMI.

[50]  Max Q.-H. Meng,et al.  A general framework for wireless capsule endoscopy study synopsis , 2015, Comput. Medical Imaging Graph..

[51]  Jachih Fu,et al.  Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging , 2014, Comput. Medical Imaging Graph..

[52]  Suzanne K. Lewis,et al.  Implementation of a polling protocol for predicting celiac disease in videocapsule analysis. , 2013, World journal of gastrointestinal endoscopy.

[53]  G. Slabaugh,et al.  Automatic Segmentation of Polyps in Colonoscopic Narrow-Band Imaging Data , 2012, IEEE Transactions on Biomedical Engineering.

[54]  Gregory D. Hager,et al.  Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images , 2012, IEEE Transactions on Biomedical Engineering.

[55]  Nwokediuko Sylvester Chuks,et al.  Challenges of Gastrointestinal Endoscopy in Resource-Poor Countries , 2011 .

[56]  I. T. Pisa,et al.  Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease , 2011, Int. J. Medical Informatics.

[57]  Andreas Uhl,et al.  Automated Marsh-like classification of celiac disease in children using local texture operators , 2011, Comput. Biol. Medicine.

[58]  Leontios J. Hadjileontiadis,et al.  Abnormal pattern detection in Wireless Capsule Endoscopy images using nonlinear analysis in RGB color space , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[59]  Thank You,et al.  USAID , 1996, The SAGE Encyclopedia of Higher Education.

[60]  Carmen C. Y. Poon,et al.  Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain , 2017, IEEE Journal of Biomedical and Health Informatics.

[61]  Mohamad Amin Pourhoseingholi,et al.  Burden of gastrointestinal cancer in Asia; an overview , 2015, Gastroenterology and hepatology from bed to bench.

[62]  小島原典子,et al.  診断精度研究のバイアスリスク評価ツールQUADAS－2：a Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies 2 の活用 , 2014 .

[63]  Gregory D. Hager,et al.  Towards integrating temporal information in capsule endoscopy image analysis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[64]  P. Hughes,et al.  The Cochrane collaboration. , 1996, Anaesthesia and intensive care.