无需专家评分，通过任务完成度自动评估胎儿超声操作技能
Learning to learn skill assessment for fetal ultrasound scanning
- 构建双层优化框架，联合训练任务预测与技能预测模型
- 在真实胎头扫描视频上实现技能水平的量化预测
- 适合医学教育与智能医疗评估系统开发者参考
传统超声技能评估依赖专家主观评判，耗时且易受主观影响。以往自动化方法多采用监督学习，分析范围受限于预设因素。本文提出一种新型双层优化框架，基于胎儿超声图像上的任务完成质量评估操作技能，无需人工定义技能等级。该框架包含临床任务预测器与技能预测器，通过协同优化两个网络实现。在真实临床胎头扫描视频数据集上验证，结果表明该方法可有效将优化后的任务表现转化为技能指标，具备实际可行性。
原文摘要 · Abstract (English)
Traditionally, ultrasound skill assessment has relied on expert supervision and feedback, a process known for its subjectivity and time-intensive nature. Previous works on quantitative and automated skill assessment have predominantly employed supervised learning methods, often limiting the analysis to predetermined or assumed factors considered influential in determining skill levels. In this work, we propose a novel bi-level optimisation framework that assesses fetal ultrasound skills by how well a task is performed on the acquired fetal ultrasound images, without using manually predefined skill ratings. The framework consists of a clinical task predictor and a skill predictor, which are optimised jointly by refining the two networks simultaneously. We validate the proposed method on real-world clinical ultrasound videos of scanning the fetal head. The results demonstrate the feasibility of predicting ultrasound skills by the proposed framework, which quantifies optimised task performance as a skill indicator.
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