Small bowel capsule endoscopy examination and open access database with artificial intelligence: The SEE-artificial intelligence project.

ABSTRACT:

Objectives

Artificial intelligence (AI) may be practical for image classification of small bowel capsule endoscopy (CE). However, creating a functional AI model is challenging. We attempted to create a dataset and an object detection CE AI model to explore modeling problems to assist in reading small bowel CE.

Methods

We extracted 18,481 images from 523 small bowel CE procedures performed at Kyushu University Hospital from September 2014 to June 2021. We annotated 12,320 images with 23,033 disease lesions, combined them with 6161 normal images as the dataset, and examined the characteristics. Based on the dataset, we created an object detection AI model using YOLO v5 and we tested validation.

Results

We annotated the dataset with 12 types of annotations, and multiple

SUBMITTER: Yokote A 

PROVIDER: S-EPMC10288072 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature