ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2023
A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).
Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 31 citations in OpenAlex.
- Artificial Intelligence for Endoscopic Diagnosis of Gastric Premalignant Lesions.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions.Diagnostics (Basel, Switzerland) · 2026Review
- Development and external validation of an interpretable multimodal deep learning model for 5-year mortality in high-risk stage ii colorectal cancer.International journal of colorectal disease · 2026Article
- Influence of sex differences among endoscopists in gastric cancer and precancerous condition detection rate during EGD in the artificial intelligence era: a retrospective study.Surgical endoscopy · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.Annals of medicine · 2025Review
- Deep learning-aided optical biopsy achieves whole-chain diagnosis of Correa cascade of gastric cancer: a prospective study.BMC medicine · 2025Article
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- [Research progress on endoscopic image diagnosis of gastric tumors based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
- Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.Journal of cancer research and clinical oncology · 2024Observational
- Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- ENDOANGELTherapeutic advances in gastroenterology · 2023Article
- A multimodal artificial intelligence system for the detection and diagnosis of solid pancreatic lesions under EUS.Endoscopic ultrasoundArticle
Corrections and comments
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Authors and funding
15 authors at 1 institution in 1 country.
Funding
Abstract
backgroundWhite light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasms using WL and WM data.
methodsWL and WM images of a same lesion were combined into image-pairs. A total of 4201 images, 7436 image-pairs, and 162 videos were used for model construction and validation. Models 1-5 including two single-modal models (WL, WM) and three multi-modal models (data fusion on task-level, feature-level, and input-level) were constructed. The models were tested on three levels including images, videos, and prospective patients. The best model was selected for constructing ENDOANGEL-MM. We compared the performance between the models and endoscopists and conducted a diagnostic study to explore the ENDOANGEL-MM's assistance ability.
resultsModel 4 (ENDOANGEL-MM) showed the best performance among five models. Model 2 performed better in single-modal models. The accuracy of ENDOANGEL-MM was higher than that of Model 2 in still images, real-time videos, and prospective patients. (86.54 vs 78.85%, P = 0.134; 90.00 vs 85.00%, P = 0.179; 93.55 vs 70.97%, P < 0.001). Model 2 and ENDOANGEL-MM outperformed endoscopists on WM data (85.00 vs 71.67%, P = 0.002) and multi-modal data (90.00 vs 76.17%, P = 0.002), significantly. With the assistance of ENDOANGEL-MM, the accuracy of non-experts improved significantly (85.75 vs 70.75%, P = 0.020), and performed no significant difference from experts (85.75 vs 89.00%, P = 0.159).
conclusionsThe multi-modal model constructed by feature-level fusion showed the best performance. ENDOANGEL-MM identified gastric neoplasms with good accuracy and has a potential role in real-clinic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.