Learning to learn skill assessment for fetal ultrasound scanning
This work addresses the need for automated, objective skill assessment in fetal ultrasound scanning, offering a quantitative alternative to expert supervision, though it appears incremental as it builds on existing automated methods.
The authors tackled the problem of subjective and time-intensive ultrasound skill assessment by developing a bi-level optimization framework that predicts fetal ultrasound scanning skills based on task performance in acquired images, without predefined skill ratings, and validated it on real-world clinical ultrasound videos.
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.