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

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.

AI support for colonoscopy quality control using CNN and transformer architectures.

Background: Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.

Methods: A total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning.