ArticleSurgical endoscopy2025
A deep neural network improves endoscopic detection of laterally spreading tumors.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.Research square · 2026Article
- Artificial intelligence-assisted diagnosis of rectal neuroendocrine tumors during white-light endoscopy.World journal of gastroenterology · 2025Article
- Eye Tracking Analysis to Determine the Endoscopist's Recognition Rate for Artificial Intelligence-Detected Sites in Colonoscopy.Digestive diseases and sciences · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Protective ileostomy creation after anterior resection of the rectum (PICARR): a decision-making exploring international survey.Updates in surgery · 2025Article
- Clinical Pathological Features and Carcinogenic Risk Factors of Colorectal Lateral Spreading Tumors With Skirt Features.Canadian journal of gastroenterology & hepatology · 2025Article
- Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study.Biomimetics (Basel, Switzerland) · 2024Article
- Retrospective study on endoscopic treatment of recurrent esophageal cancer patients after radiotherapy.Surgical endoscopy · 2024Article
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20 authors.
Funding
Abstract
backgroundColorectal cancer (CRC) is the malignant tumor of the digestive system with the highest incidence and mortality rate worldwide. Laterally spreading tumors (LSTs) of the large intestine have unique morphological characteristics, special growth patterns and higher malignant potential. Therefore, LSTs are a precancerous lesion of CRC that could be easily missed.
objectiveThe purpose of this study was to establish an LSTs lesion detection algorithm based on the YOLOv7 model and to evaluate the detection performance of the algorithm on LSTs.
methodA total of 7985 LSTs images and 93,197 non-LSTs images were included in this study, and the training set, validation set, and 80% of the data in the dataset is used for training, 10% for validation, and 10% for testing. In detail, a total of 6261 LSTs images and 74,798 non-LSTs images were used as the training set to train the LSTs lesion detection algorithm to identify LSTs. A total of 743 LSTs images and 9486 non-LSTs images were used as validation set to evaluate the learning ability of the LSTs lesion detection algorithm. A total of 981 LSTs images and 8913 non-LSTs images were used as test set to evaluate the generalization ability of the LSTs lesion detection algorithm. To evaluate the diagnostic ability of the LSTs lesion detection algorithm for LSTs, we selected 3636 images (562 LSTs, 3074 non-LSTs) images from the test set as the subtest set. Finally, we compared the performance of the AI algorithm with endoscopist in the diagnosis of LSTs.
resultThe accuracy of LSTs lesion detection algorithm in identifying LSTs is 99.34%, sensitivity is 96.88%, specificity is 99.8%, positive predictive value is 98.94%, and negative predictive value is 99.41%.
conclusionOur model based on the YOLOv7 achieved high diagnostic accuracy in LSTs lesion, significantly better than that of novice and senior doctors, and reaching the same level as expert endoscopists.
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