B0641 - Reduction of Video Capsule Endoscopy Reading Times Using Artificial Intelligence

Azubuogu Anudu, MD¹, Uche Chukwudumebi, DO², Warman Roshan, BS³, Hunter Morera, MSs⁴, Reddy Nikhil, MS⁵, Ivana Radosavljevic, BS¹, Niketa Patel, PhD⁶, Patrick Brady, PhD², Jose Lezama, MD⁶, Dmitry Goldgof, PhD², Lawrence Hall, PhD², Gitanjali Vidyarthi, MD^6
1University of South Florida Morsani College of Medicine, Tampa, FL; ²University of South Florida, Tampa, FL; ³Yale University School of Medicine, New Haven, CT; ⁴University of South Florida, Tamoa, FL; ⁵Morsani College of Medicine, Tampa, FL; ⁶James A. Haley VA Hospital, Tampa, FL

Introduction: 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. The purpose of this study is to create a machine learning model capable of significantly reducing capsule endoscopy reading times.

Methods: In this study, we have trained a convolutional neural network, ResNet50, using videos from the KID dataset to confidently exclude normal images on VCE, while retaining abnormal ones. The CNN was exposed to 3 full length capsule endoscopy videos A threefold cross-validation scheme was employed whereby the model was trained using two of the aforementioned videos, tested on the third and this was repeated for all possible video combinations. We trained our model for nine epochs and further improved the predictions of our model by adding rotated versions of abnormal segments to our training data.

Results: Our study identified abnormal frames in 3 KID videos as outlined in Table 1. We were able to reduce the video length by 47%, on average, and captured frames from 118 of the 119 abnormal segments labeled by the expert physician. We were able to develop an algorithm that successfully detected 99% of abnormal segments while reducing the reading time for a physician by over 43%.

Discussion: Our model demonstrated high levels of accuracy with significant reduction in physician reading time. Our results are reassuring and demonstrate the benefit of CNN in processing VCE images. We believe our study lays an excellent foundation for further validation in large multicenter trials



Disclosures:


Azubuogu Anudu, MD¹, Uche Chukwudumebi, DO², Warman Roshan, BS³, Hunter Morera, MSs⁴, Reddy Nikhil, MS⁵, Ivana Radosavljevic, BS¹, Niketa Patel, PhD⁶, Patrick Brady, PhD², Jose Lezama, MD⁶, Dmitry Goldgof, PhD², Lawrence Hall, PhD², Gitanjali Vidyarthi, MD⁶. B0641 - Reduction of Video Capsule Endoscopy Reading Times Using Artificial Intelligence, ACG 2022 Annual Scientific Meeting Abstracts. Charlotte, NC: American College of Gastroenterology.