用AI实时检测手术器械缺陷，准确率99.3%且每图仅需5毫秒。
Real-Time Surgical Instrument Defect Detection via Non-Destructive Testing
- 基于YOLOv8的实时缺陷检测框架，支持11类器械、5类缺陷
- 在10万+图像上训练，准确率达99.3%，单图推理快至5.8毫秒
- 提升视觉检测效果，适合医疗制造自动化与合规生产
有缺陷的手术器械会严重威胁无菌性、机械完整性和患者安全，增加手术并发症风险。然而，目前手术器械制造的质量控制仍依赖人工检查，易受人为误差和不一致影响。本研究提出SurgScan——一种基于AI的手术器械缺陷检测框架。采用YOLOv8模型，实现缺陷的实时分类，兼具高精度与工业可扩展性。模型在包含102,876张图像的高分辨率数据集上训练，覆盖11种器械类型和5类主要缺陷。与主流CNN架构对比评估显示，SurgScan达到最高准确率（99.3%），单图推理速度为4.2–5.8毫秒，适合工业部署。统计分析表明，对比度增强预处理显著提升缺陷检测性能，有效克服视觉检测的关键局限。SurgScan提供一种可扩展、低成本的自动化质量控制方案，降低对人工检查的依赖，同时满足ISO 13485和FDA标准，推动医疗制造中缺陷检测能力的提升。
原文摘要 · Abstract (English)
Defective surgical instruments pose serious risks to sterility, mechanical integrity, and patient safety, increasing the likelihood of surgical complications. However, quality control in surgical instrument manufacturing often relies on manual inspection, which is prone to human error and inconsistency. This study introduces SurgScan, an AI-powered defect detection framework for surgical instruments. Using YOLOv8, SurgScan classifies defects in real-time, ensuring high accuracy and industrial scalability. The model is trained on a high-resolution dataset of 102,876 images, covering 11 instrument types and five major defect categories. Extensive evaluation against state-of-the-art CNN architectures confirms that SurgScan achieves the highest accuracy (99.3%) with real-time inference speeds of 4.2-5.8 ms per image, making it suitable for industrial deployment. Statistical analysis demonstrates that contrast-enhanced preprocessing significantly improves defect detection, addressing key limitations in visual inspection. SurgScan provides a scalable, cost-effective AI solution for automated quality control, reducing reliance on manual inspection while ensuring compliance with ISO 13485 and FDA standards, paving the way for enhanced defect detection in medical manufacturing.
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