ArticleBMC medical informatics and decision making2025
Deep learning model applied to real-time delineation of colorectal polyps.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep multimodal fusion of patho-radiomic and clinical data for enhanced survival prediction for colorectal cancer patients.NPJ digital medicine · 2025Article
- Machine learning in gastrointestinal endoscopy: challenges and opportunities.BMJ open gastroenterology · 2025Review
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Authors and funding
7 authors.
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Abstract
backgroundDeep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its application in medical settings remains underexplored. This study evaluated the performance of a YOLACT-derived Real-time Polyp Delineation Model (RTPoDeMo) for real-time use on prospectively recorded colonoscopy videos.
methodsTwelve combinations of architectures, including Mask-RCNN, YOLACT, and YOLACT++, paired with backbones such as ResNet50, ResNet101, and DarkNet53, were tested on 2,188 colonoscopy images with three image resolution sizes. Dataset preparation involved pre-processing and segmentation annotation, with optimized image augmentation.
resultsRTPoDeMo, using YOLACT-ResNet50, achieved 72.3 mAP and 32.8 FPS for real-time instance segmentation based on COCO annotations. The model performed with a per-image accuracy of 99.59% (95% CI: [99.45 - 99.71%]), sensitivity of 90.63% (95% CI: [78.95 - 93.64%]), specificity of 99.95% (95% CI: [99.93 - 99.97%]) and a F1-score of 0.94 (95% CI: [0.87-0.98]). In validation, out of 36 polyps detected by experts, RTPoDeMo missed only one polyp, compared to six missed by senior endoscopists. The model demonstrated good agreement with experts, reflected by a Cohen's Kappa coefficient of 0.72 (95% CI: [0.54-1.00], p < 0.0001).
conclusionsOur model provides new perspectives in the adaptation of YOLACT to the real-time delineation of colorectal polyps. In the future, it could improve the characterization of polyps to be resected during colonoscopy.
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