Related Experiment Video
Updated: Dec 19, 2025
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers: comparison between the
Ken Namikawa1, Toshiaki Hirasawa1,2, Kaoru Nakano1,3
1Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
Background:
We previously reported for the first time the usefulness of artificial intelligence (AI) systems in detecting gastric cancers. However, the "original convolutional neural network (O-CNN)" employed in the previous study had a relatively low positive predictive value (PPV). Therefore, we aimed to develop an advanced AI-based diagnostic system and evaluate its applicability for the classification of gastric cancers and gastric ulcers.
Methods:
We constructed an "advanced CNN" (A-CNN) by adding a new training dataset (4453 gastric ulcer images from 1172 lesions) to the O-CNN, which had been trained using 13 584 gastric cancer and 373 gastric ulcer images. The diagnostic performance of the A-CNN in terms of classifying gastric cancers and ulcers was retrospectively evaluated using an independent validation dataset (739 images from 100 early gastric cancers and 720 images from 120 gastric ulcers) and compared with that of the O-CNN by estimating the overall classification accuracy.
Results:
The sensitivity, specificity, and PPV of the A-CNN in classifying gastric cancer at the lesion level were 99.0 % (95 % confidence interval [CI] 94.6 %-100 %), 93.3 % (95 %CI 87.3 %-97.1 %), and 92.5 % (95 %CI 85.8 %-96.7 %), respectively, and for classifying gastric ulcers were 93.3 % (95 %CI 87.3 %-97.1 %), 99.0 % (95 %CI 94.6 %-100 %), and 99.1 % (95 %CI 95.2 %-100 %), respectively. At the lesion level, the overall accuracies of the O- and A-CNN for classifying gastric cancers and gastric ulcers were 45.9 % (gastric cancers 100 %, gastric ulcers 0.8 %) and 95.9 % (gastric cancers 99.0 %, gastric ulcers 93.3 %), respectively.
Conclusion:
The newly developed AI-based diagnostic system can effectively classify gastric cancers and gastric ulcers.
More Related Videos
03:05Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
01:28Peptic Ulcer Disease III: Clinical Manifestations and Diagnostic Studies
Few clinical manifestations differentiate gastric ulcers from duodenal ulcers. Distinctions in the location, timing, and pain relief are crucial for healthcare providers in differentiating between gastric and duodenal ulcers during clinical assessments.
01:50What is Monogastric Digestion?
01:17Assessment of the Gastrointestinal System I: Subjective Data
Health History
The initial step in assessing the GI system is obtaining a comprehensive health history. This includes inquiring about the patient's history or presence of problems...