AI实时检测胃肠道息肉，提升内镜诊断准确率。
EndoSight AI: Deep Learning-Driven Real-Time Gastrointestinal Polyp Detection and Segmentation for Enhanced Endoscopic Diagnostics
- 基于深度学习的实时检测与分割框架
- 检测mAP达88.3%，分割Dice系数达69%
- 适合内镜医生辅助诊断，部署于真实医疗场景
内镜检查中精准实时地检测胃肠道息肉对早期诊断和预防结直肠癌至关重要。本文提出EndoSight AI，一种独立开发并评估的深度学习架构，用于实现息肉的准确定位与边界精细分割。该系统基于公开的Hyper-Kvasir数据集训练，在GPU硬件上实现超过35帧/秒的实时推理速度，检测的平均精度（mAP）达到88.3%，分割的Dice系数最高达69%。训练过程引入临床相关性能指标及新型热感知流程，确保模型鲁棒性与效率。该集成式AI方案可无缝嵌入内镜工作流，有望提升胃肠道诊疗的准确性与临床决策能力。
原文摘要 · Abstract (English)
Precise and real-time detection of gastrointestinal polyps during endoscopic procedures is crucial for early diagnosis and prevention of colorectal cancer. This work presents EndoSight AI, a deep learning architecture developed and evaluated independently to enable accurate polyp localization and detailed boundary delineation. Leveraging the publicly available Hyper-Kvasir dataset, the system achieves a mean Average Precision (mAP) of 88.3% for polyp detection and a Dice coefficient of up to 69% for segmentation, alongside real-time inference speeds exceeding 35 frames per second on GPU hardware. The training incorporates clinically relevant performance metrics and a novel thermal-aware procedure to ensure model robustness and efficiency. This integrated AI solution is designed for seamless deployment in endoscopy workflows, promising to advance diagnostic accuracy and clinical decision-making in gastrointestinal healthcare.
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