ArticleJournal of gastroenterology2024
A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer.
Article in Journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- The SHAP Has Sailed: Explainable Machine Learning Meets Endoscopic Ultrasonography in the Diagnosis of Non-pancreatic Periampullary Lesions.Digestive diseases and sciences · 2026Article
- Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions.Diagnostics (Basel, Switzerland) · 2026Review
- Comment on "A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer".Journal of gastroenterology · 2026Article
- Value of Endoscopic Ultrasonography for Distinguishing Malignant from Benign Non-pancreatic Periampullary Lesions: An Explainable Machine Learning Study.Digestive diseases and sciences · 2026Article
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Improved diagnostic accuracy and safety of gel-endoscopic ultrasonography for deep submucosal invasion in early gastric cancer.Surgical endoscopy · 2026Article
- Performance of an Artificial Intelligence Program for Assessing the Depth of Early Gastric Cancer Using Endoscopic Images.Endoscopy international open · 2026Article
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Endoscopic Ultrasound (EUS) in Gastric Cancer: Current Applications and Future Perspectives.Diseases (Basel, Switzerland) · 2025Review
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Risk factors and predictive nomogram for non-curative resection in patients with early gastric cancer treated with endoscopic submucosal dissection: a retrospective cohort study.World journal of surgical oncology · 2025Article
- Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Validation of multiple deep learning models for colorectal tumor differentiation with endoscopic ultrasound images: a dual-center study.Journal of gastrointestinal oncology · 2025Article
- Invasion and metastasis in cancer: molecular insights and therapeutic targets.Signal transduction and targeted therapy · 2025Review
- Review
- What can artificial intelligence do for EUS?Endoscopic ultrasoundArticle
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18 authors.
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Abstract
backgroundWe developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.
methodsA total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).
resultsThe area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.
conclusionsOur AI model demonstrated a diagnostic performance equivalent to that of experts.
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