A Deep Learning Model for Diagnosing Gastric Mucosal Lesions Using Endoscopic Images: Development, Validation, and Method Comparison

Abstract

Endoscopic differential diagnoses of gastric mucosal lesions remain challenging. We aimed to develop and validate convolutional neural network-based artificial intelligence models-lesion detection, differential diagnosis, and invasion-depth models. The AI-DDx showed good diagnostic performance for both internal and external validation. The performance of the AI-DDx was better than that of the novice and intermediate endoscopists, but was comparable to the experts in the external validation set. The AI-ID showed fair performances in both internal and external validation sets, which were significantly better than EUS results performed by experts.

Publication
in SSRN
Hyungjin Chung
Hyungjin Chung
Ph.D. student - Deep Learning & Inverse Problems

My research interests include, but is not restricted to solving inverse problems (MRI, tomography, microscopy, phase retrieval, etc.) via deep learning

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