用AI自动评估缝合技能，实时反馈提升训练效率
AI-driven Automation of End-to-end Assessment of Suturing Expertise
- 基于AI模型端到端预测7个缝合技能维度得分
- 实测在低资源下实现秒级评分，准确率超90%
- 适合手术培训系统与智能医疗设备集成
我们提出一种基于AI的端到端缝合技能评估方法（EASE），该工具全面定义了缝合相关子技能的评估标准。尽管EASE能为学员提供客观的能力评估和可操作建议，但当前评分仍依赖人工，耗时耗力。本文提出的AI方法可在推理阶段以极低资源实现实时分数预测，支持术者/学员即时反馈，加速学习进程并减少术中失误，改善患者预后。研究聚焦于三个缝合阶段下的7个EASE评估维度：1）持针：重定位次数、持针深度、持针比例、持针角度；2）进针：平滑度、腕部旋转；3）拔针：腕部旋转。
原文摘要 · Abstract (English)
We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills.1 While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes. In this study, we focus on the following 7 EASE domains that come under 3 suturing phases: 1) Needle Handling: Number of Repositions, Needle Hold Depth, Needle Hold Ratio, and Needle Hold Angle; 2) Needle Driving: Driving Smoothness, and Wrist Rotation; 3) Needle Withdrawal: Wrist Rotation.
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