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

Abstract : Background and Aims Gastrointestinal angiectasia (GIA) is the most common small bowel (SB) vascular lesion, with an inherent risk of bleeding. SB Capsule Endoscopy (SB-CE) is the currently accepted diagnostic procedure. The aim of this study was to develop a computer-assisted diagnosis (CAD) tool for the detection of GIA. Methods Deidentified SB-CE still frames featuring annotated typical GIA and normal control still frames, were selected from a database. A semantic segmentation images approach associated with a convolutional neural network (CNN) was used for deep feature extractions and classification. Two datasets of still frames were created and used for machine-learning and for algorithm testing. Results The GIA detection algorithm yielded a sensitivity of 100%, a specificity of 96%, a positive predictive value of 96%, and a negative predictive value of 100%. Reproducibility was optimal. The reading process for an entire SB-CE video would take 2340 seconds (39 minutes). Conclusion The developed CNN-based algorithm had high diagnostic performances allowing detection of GIA in SB-CE still frames. This study paves the way for future automated CNN-based SB-CE reading softwares.
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Submitted on : Wednesday, July 11, 2018 - 1:26:17 PM
Last modification on : Friday, August 5, 2022 - 2:45:59 PM



Romain Leenhardt, Pauline Vasseur, Cynthia Li, Gabriel Rahmi, Franck Cholet, et al.. A neural 
network algorithm for detection of GI angiectasia during 
small-bowel capsule endoscopy. Gastrointestinal Endoscopy, 2019, 89 (1), pp.189-194. ⟨10.1016/j.gie.2018.06.036⟩. ⟨hal-01835422⟩



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