Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading

• Published in: PloS one 2020-10, Vol.15 (10), p.e0241474-e0241474
• Main Authors: Park, Junseok, Hwang, Youngbae, Nam, Ji Hyung, Oh, Dong Jun, Kim, Ki Bae, Song, Hyun Joo, Kim, Su Hwan, Kang, Sun Hyung, Jung, Min Kyu, Jeong Lim, Yun
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial Intelligence; Capsule Endoscopy; Classification; Clinical significance; Computer aided medical diagnosis; Datasets; Deep learning; Diagnostic imaging; Endoscopy; Gastroenterology; Hepatology; Hospitals; Humans; Image Processing, Computer-Assisted - methods; Internal medicine; Lesions; Medical schools; Medicine; Methods; Object recognition; Performance evaluation; Reading; Time Factors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0241474

Artificial intelligence (AI), which has demonstrated outstanding achievements in image recognition, can be useful for the tedious capsule endoscopy (CE) reading. We aimed to develop a practical AI-based method that can identify various types of lesions and tried to evaluate the effectiveness of the method under clinical settings. A total of 203,244 CE images were collected from multiple centers selected considering the regional distribution. The AI based on the Inception-Resnet-V2 model was trained with images that were classified into two categories according to their clinical significance. The performance of AI was evaluated with a comparative test involving two groups of reviewers with different experiences. The AI summarized 67,008 (31.89%) images with a probability of more than 0.8 for containing lesions in 210,100 frames of 20 selected CE videos. Using the AI-assisted reading model, reviewers in both the groups exhibited increased lesion detection rates compared to those achieved using the conventional reading model (experts; 34.3%-73.0%; p = 0.029, trainees; 24.7%-53.1%; p = 0.029). The improved result for trainees was comparable to that for the experts (p = 0.057). Further, the AI-assisted reading model significantly shortened the reading time for trainees (1621.0-746.8 min; p = 0.029). Thus, we have developed an AI-assisted reading model that can detect various lesions and can successfully summarize CE images according to clinical significance. The assistance rendered by AI can increase the lesion detection rates of reviewers. Especially, trainees could improve their efficiency of reading as a result of reduced reading time using the AI-assisted model.