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Aim <p>To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during endoscopy, with the goal of enhancing diagnostic precision.</p> Methods <p>This was a multicenter, retrospective study collecting endoscopic images and videos from four tertiary hospitals in China. An improved YOLOv8 model, incorporating an illumination attention module, was developed for real-time instance segmentation and classification. The dataset comprised 9,820 benign ulcer images, 1,727 malignant ulcer images, and 15,791 normal mucosa images, split into training, testing, and validation sets at an 8:1:1 ratio. Performance was evaluated based on precision, recall, specificity, and processing latency.</p> Results <p>On the validation set, the AI model achieved an overall precision, recall, and specificity of 0.91, 0.91, and 0.95, respectively. For malignant ulcer recognition specifically, the precision, recall, and specificity were 0.90, 0.91, and 0.99. The model demonstrated strong real-time performance with a latency of 8.84 ms per frame and a processing speed of 113 frames per second.</p> Conclusion <p>The developed AI model enables accurate, real-time discrimination between benign and malignant gastric ulcers during endoscopy. It holds potential to augment clinical decision-making, standardize diagnostic quality, and optimize biopsy strategies.</p>

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Development and validation of a real-time AI model for differentiating benign and malignant gastric ulcers : a multicenter retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Yibo Tan,
  • Yongjun Wu,
  • Mei Yang,
  • Yan Li,
  • Xiaofei Bi,
  • Song He,
  • Zhihang Zhou,
  • Junyu Lu

摘要

Aim

To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during endoscopy, with the goal of enhancing diagnostic precision.

Methods

This was a multicenter, retrospective study collecting endoscopic images and videos from four tertiary hospitals in China. An improved YOLOv8 model, incorporating an illumination attention module, was developed for real-time instance segmentation and classification. The dataset comprised 9,820 benign ulcer images, 1,727 malignant ulcer images, and 15,791 normal mucosa images, split into training, testing, and validation sets at an 8:1:1 ratio. Performance was evaluated based on precision, recall, specificity, and processing latency.

Results

On the validation set, the AI model achieved an overall precision, recall, and specificity of 0.91, 0.91, and 0.95, respectively. For malignant ulcer recognition specifically, the precision, recall, and specificity were 0.90, 0.91, and 0.99. The model demonstrated strong real-time performance with a latency of 8.84 ms per frame and a processing speed of 113 frames per second.

Conclusion

The developed AI model enables accurate, real-time discrimination between benign and malignant gastric ulcers during endoscopy. It holds potential to augment clinical decision-making, standardize diagnostic quality, and optimize biopsy strategies.