Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm.
Gastrointest Endosc
; 96(5): 787-795.e6, 2022 11.
Article
en En
| MEDLINE
| ID: mdl-35718070
ABSTRACT
BACKGROUND AND AIMS:
The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted diagnosis system (GEADS) to improve diagnostic accuracy and clinical utility.METHODS:
In this retrospective, multicenter study, a convolutional neural network was trained to assess upper GI diseases based on 26,228 endoscopic images from Dazhou Central Hospital that were randomly assigned (311) to a training dataset, validation dataset, and test dataset, respectively. To validate the model, 6 external independent datasets comprising 51,372 images of upper GI diseases were collected. In addition, 1 prospective dataset comprising 27,975 images was collected. The performance of GEADS was compared with endoscopists with 2 professional degrees of expertise expert and novice. Eight endoscopists were in the expert group with >5 years of experience, whereas 3 endoscopists were in the novice group with 1 to 5 years of experience.RESULTS:
The GEADS model achieved an accuracy of .918 (95% confidence interval [CI], .914-.922), with an F1 score of .884 (95% CI, .879-.889), recall of .873 (95% CI, .868-.878), and precision of .890 (95% CI, .885-.895) in the internal validation dataset. In the external validation datasets and 1 prospective validation dataset, the diagnostic accuracy of the GEADS ranged from .841 (95% CI, .834-.848) to .949 (95% CI, .935-.963). With the help of the GEADS, the diagnosing accuracies of novice and expert endoscopists were significantly improved (P < .001).CONCLUSIONS:
The AI system can assist endoscopists in improving the accuracy of diagnosing upper GI diseases.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Enfermedades Gastrointestinales
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
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Prognostic_studies
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Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Gastrointest Endosc
Año:
2022
Tipo del documento:
Article
País de afiliación:
China