Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).

ABSTRACT:

Background

Although capsule endoscopy (CE) is a crucial tool for diagnosing small bowel diseases, the need to process a vast number of images imposes a significant workload on physicians, leading to a high risk of missed diagnoses. This study aims to develop an artificial intelligence (AI) model and application based on convolutional neural networks that can automatically recognize various lesions in small bowel capsule endoscopy.

Methods

Three small bowel capsule endoscopy datasets were used for AI model training, validation, and testing, encompassing 12 categories of images. The model's performance was evaluated using metrics such as AUC, sensitivity, specificity, precision, accuracy, and F1 score to select the best model. A human-machine comparison experiment was conducte

SUBMITTER: Chen J 

PROVIDER: S-EPMC11539301 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature