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Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and

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Artificial intelligence (AI) models accurately differentiate gastric mucosal lesions, outperforming less experienced endoscopists. AI also better predicts cancer invasion depth than endoscopic ultrasound (EUS).

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Distinguishing between benign gastric ulcers, early gastric cancer (EGC), and advanced gastric cancer via endoscopy is challenging.
  • Convolutional neural network (CNN)-based artificial intelligence (AI) models offer potential for improved diagnostic accuracy.

Purpose of the Study:

  • To develop and validate AI models for detecting gastric mucosal lesions, differentiating diagnoses (AI-DDx), and predicting invasion depth (AI-ID).
  • To compare the performance of these AI models against endoscopists with varying experience levels and endoscopic ultrasound (EUS).

Main Methods:

  • 1366 patients with gastric mucosal lesions from Korean referral centers were included, using one representative endoscopic image per patient.
  • AI models for differential diagnosis (AI-DDx) and invasion depth (AI-ID) were trained and validated.
  • AI model performance was compared to visual diagnoses by novice, intermediate, and expert endoscopists, and to EUS results for invasion depth.

Main Results:

  • The AI-DDx model achieved an area under the receiver operating characteristic curve (AUROC) of 0.86 in both internal and external validation.
  • In external validation, AI-DDx outperformed novice (AUROC=0.82) and intermediate (AUROC=0.84) endoscopists, performing comparably to experts (AUROC=0.89).
  • The AI-ID model demonstrated fair performance (AUROC=0.73 in external validation) and significantly outperformed expert EUS (AUROC=0.56).

Conclusions:

  • AI-DDx models demonstrate comparable performance to expert endoscopists and superior performance to novice and intermediate endoscopists in diagnosing gastric mucosal lesions.
  • AI-ID models show better accuracy than EUS in evaluating the invasion depth of gastric cancer.