Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field
This work addresses surgical training and quality control by providing an automated assessment method for in-vivo laparoscopic surgeries, though it is incremental as it builds on existing skill assessment concepts with a new proxy.
The paper tackled surgical skill assessment by identifying clearness of operating field as a proxy for skill and proposing a neural network framework to predict skills, achieving a Spearman's correlation of 0.55 with ground truth, comparable to junior surgeons.
Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills and high inter-annotator consistency. Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF. The neural network is jointly trained with a supervised regression loss and an unsupervised rank loss. In experiments, the proposed method achieves 0.55 Spearman's correlation with the ground truth of overall technical skill, which is even comparable with the human performance of junior surgeons.