A multisystem-compatible deep learning-based algorithm for detection and characterization of angiectasias in small-bowel capsule endoscopy. A proof-of-concept study.

Background and aims. Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic). Material and Methods. An advanced AI solution (Convolutional neural network), previously trained on on Pillcam® small bowel images, was evaluated on independent datasets with more than 1200 Pillcam® and MiroCam® still frames (equally distributed, with or without angiectasias). Images were reviewed by experts before and after AI interpretation. Results. Sensitivity for the diagnosis of angiectasia was 97.4% with Pillcam® images and 96.1% with Mirocam® images, with specificity of 98.8% and 97.8%, respectively. Performances regarding the delineation of regions of interest and the characterization of angiectasias were similar in both groups (all above 95%). Processing time was significantly shorter with Mirocam® (20.7 ms) than with Pillcam® images (24.6 ms, p<0.0001), possibly related to technical differences between systems. Conclusion. This proof-of-concept study on still images paves the way for the development of resource-sparing, “universal” CE databases and AI solutions for CE interpretation.

Citation

Houdeville, C., Souchaud, M., Leenhardt, R., Beaumont, H., Benamouzig, R., McAlindon, M., Grimbert, S., Lamarque, D., Makins, R., Saurin, J.-C., Histace, A., & Dray, X. (2021). A multisystem-compatible deep learning-based algorithm for detection and characterization of angiectasias in small-bowel capsule endoscopy: A proof-of-concept study. Digestive and Liver Disease, 53(12), 1627–1631. https://doi.org/10.1016/j.dld.2021.08.026

DOI

10.1016/j.dld.2021.08.026

URI

https://hdl.handle.net/20.500.14709/828

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