Your browser doesn't support javascript.
loading
Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.
Li, Lan; Yang, Liping; Zhang, Bingling; Yan, Guofei; Bao, Yaqing; Zhu, Renke; Li, Shengjie; Wang, Huogen; Chen, Ming; Jin, Chaohui; Chen, Yishu; Yu, Chaohui.
Afiliación
  • Li L; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China. Electronic address: nalil@zju.edu.cn.
  • Yang L; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
  • Zhang B; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
  • Yan G; Zhejiang Center for Medical Device Evaluation, Hangzhou, China.
  • Bao Y; GBA Center for Medical Device Evaluation and Inspection, National Medical Products Administration, Shenzhen, China.
  • Zhu R; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
  • Li S; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
  • Wang H; Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China.
  • Chen M; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China.
  • Jin C; Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China.
  • Chen Y; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
  • Yu C; Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
Clin Res Hepatol Gastroenterol ; 48(5): 102334, 2024 May.
Article en En | MEDLINE | ID: mdl-38582328
ABSTRACT

BACKGROUND:

In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE.

METHODS:

A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively.

RESULTS:

The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001).

CONCLUSIONS:

CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Endoscopía Capsular / Aprendizaje Profundo / Intestino Delgado Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Clin Res Hepatol Gastroenterol Año: 2024 Tipo del documento: Article Pais de publicación: Francia
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Endoscopía Capsular / Aprendizaje Profundo / Intestino Delgado Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Clin Res Hepatol Gastroenterol Año: 2024 Tipo del documento: Article Pais de publicación: Francia