Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma.
J Gastroenterol Hepatol
; 36(1): 131-136, 2021 Jan.
Article
en En
| MEDLINE
| ID: mdl-32511793
ABSTRACT
BACKGROUND AND AIM:
Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system using deep learning for the detection of E/J cancers.METHODS:
A total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non-cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board-certified specialists (experts).RESULTS:
The sensitivity, specificity, and accuracy of the AI system were 94%, 42%, and 66%, respectively, and that of the experts were 88%, 43%, and 63%, respectively. The sensitivity of the AI system was favorable, while its specificity for non-cancerous lesions was similar to that of the experts. Interobserver agreement among the experts for detecting superficial E/J was fair (Fleiss' kappa = 0.26, z = 20.4, P < 0.001).CONCLUSIONS:
Our AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Neoplasias Gástricas
/
Procesamiento de Imagen Asistido por Computador
/
Neoplasias Esofágicas
/
Inteligencia Artificial
/
Adenocarcinoma
/
Unión Esofagogástrica
/
Detección Precoz del Cáncer
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Screening_studies
Límite:
Adult
/
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
País/Región como asunto:
Asia
Idioma:
En
Revista:
J Gastroenterol Hepatol
Asunto de la revista:
GASTROENTEROLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Japón