Christian Maaser,a Andreas Sturm,b Stephan R. Vavricka,c Torsten Kucharzik,d Gionata Fiorino,e Vito Annese,f Emma Calabrese,g Daniel C. Baumgart,h Dominik Bettenworth,i Paula Borralho Nunes,j, Johan Burisch,k, Fabiana Castiglione,l Rami Eliakim,m Pierre Ellul,n Yago González-Lama,o Hannah Gordon,p Steve Halligan,q Konstantinos Katsanos,r Uri Kopylov,m Paulo G. Kotze,s Eduards Krustiņš,t Andrea Laghi,u Jimmy K. Limdi,v Florian Rieder,w Jordi Rimola,x Stuart A. Taylor,y Damian Tolan,z Patrick van Rheenen,aa Bram Verstockt,bb, Jaap Stokercc; on behalf of the European Crohn’s and Colitis Organisation [ECCO] and the European Society of Gastrointestinal and Abdominal Radiology [ESGAR]

https://www.zora.uzh.ch/id/eprint/181209/9/2018_05_02_Manuscript1_ECCO_ESGAR-DiagnosticsGL_c1-3.pdf

ECCO-ESGAR Guideline for Diagnostic Assessment in IBD Part 1: Initial diagnosis, monitoring of known IBD, detection of complications

Clinical Practice Guidelines for the Use of Video Capsule Endoscopy

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1002/hep.22273

Portal hypertension and variceal bleeding—Unresolved issues. Summary of an American Association for the study of liver diseases and European Association for the study of the liver single‐topic conference

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at Baerum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The HyperKvasir dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.

/pdf/hyperkvasir-a-comprehensive-multi-class-image-and-video-5a4535kwsq.pdf

HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy

Background and aims Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training Current databases lack graphic annotations of pathologies and cannot be used A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE Methods Open-source software was used for the KID database Clinicians contribute anonymized, annotated CE images and videos Graphic annotations are supported by an open-access annotation tool (Ratsnake) We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers Results The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 % The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %) Conclusion MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL) Automated lesion measurement is therefore feasible KID is currently the only open-source CE database developed specifically to aid development of MDSS Our experiment demonstrates this potential

/pdf/kid-project-an-internet-based-digital-video-atlas-of-capsule-4rlp0jncay.pdf

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes

https://www.giejournal.org/article/S0016-5107(15)02502-X/pdf

Prediction of patency capsule retention in known Crohn's disease patients by using magnetic resonance imaging

Capsule Endoscopy and Deep Enteroscopy in Inflammatory Bowel Disease

Esophageal Capsule Endoscopy (PillCam ESO) Is Comparable to Traditional Endoscopy for Detection of Esophageal Varices-An International Multi-Center Trial

Abraham R. Eliakim

Papers

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes

Prediction of patency capsule retention in known Crohn's disease patients by using magnetic resonance imaging

Capsule Endoscopy and Deep Enteroscopy in Inflammatory Bowel Disease

Esophageal Capsule Endoscopy (PillCam ESO) Is Comparable to Traditional Endoscopy for Detection of Esophageal Varices-An International Multi-Center Trial

336 PAtency Capsule in Patients With Established Crohn's Disease Undergoing Videocapsule Endoscopy of the Small Bowel