Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data

Autor: Hunter Morera, Roshan Warman, Azubuogu Anudu, Chukwudumebi Uche, Ivana Radosavljevic, Nikhil Reddy, Ahan Kayastha, Niharika Baviriseaty, Rahul Mhaskar, Andrew A. Borkowski, Patrick Brady, Satish Singh, Gerard Mullin, Jose Lezama, Lawrence O. Hall, Dmitry Goldgof, Gitanjali Vidyarthi
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Algorithms, Vol 15, Iss 10, p 339 (2022)
Druh dokumentu: article
ISSN: 1999-4893
57572283
DOI: 10.3390/a15100339
Popis: Video capsule endoscopy (VCE) is an innovation that has revolutionized care within the field of gastroenterology, but the time needed to read the studies generated has often been cited as an area for improvement. With the aid of artificial intelligence, various fields have been able to improve the efficiency of their core processes by reducing the burden of irrelevant stimuli on their human elements. In this study, we have created and trained a convolutional neural network (CNN) capable of significantly reducing capsule endoscopy reading times by eliminating normal parts of the video while retaining abnormal ones. Our model, a variation of ResNet50, was able to reduce VCE video length by 47% on average and capture abnormal segments on VCE with 100% accuracy on three VCE videos as confirmed by the reading physician. The ability to successfully pre-process VCE footage as we have demonstrated will greatly increase the practicality of VCE technology without the expense of hundreds of hours of physician annotated videos.
Databáze: Directory of Open Access Journals
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