Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm.
Gut
; 71(12): 2388-2390, 2022 12.
Article
en En
| MEDLINE
| ID: mdl-36109151
ABSTRACT
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Resección Endoscópica de la Mucosa
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Gut
Año:
2022
Tipo del documento:
Article
País de afiliación:
Alemania