A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.Leenhardt R, Vasseur P, Li C, Saurin JC, Rahmi G, Cholet F, Becq A, Marteau P, Histace A, Dray X; CAD-CAP Database Working Group. Gastrointest Endosc. 2018 Jul 11 [Epub ahead of print]

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 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. Leggi l'articolo

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