Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field

TL;DR

This paper introduces an automated neural network framework to assess surgical skills in real clinical data by analyzing the clearness of the operating field, showing strong correlation with expert evaluations.

Contribution

It identifies the clearness of the operating field as a reliable proxy for surgical skill and develops a neural network model to predict skills based on this proxy in real surgeries.

Findings

Achieves 0.55 Spearman's correlation with ground truth

Comparable performance to junior surgeons

Validates COF as a skill proxy

Abstract

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.