From Feline Classification to Skills Evaluation: A Multitask Learning Framework for Evaluating Micro Suturing Neurosurgical Skills

Abstract

Automated skill evaluation of a trainee is key to the utility of the surgical training system. The focus of this paper is to develop an automated tool for the assessment of trainees for micro-suturing task. The real-life training datasets for the micro-suturing task are often small, with long-tailed distribution, making it difficult to develop machine-learning-based tools for automated assessment. Further, micro-suturing is often performed at various magnifications and suture sizes, which makes the automated assessment more challenging compared to macro-suturing. Hence, currently, assessment is done manually by an expert using the final outcome image. In this paper, we propose a multi-task learning-based convolutional-neural-network regression model to score the effectualness of the micro-suturing task from the final outcome image. We propose a novel equivalent of the logit-adjustment (used in classification) applicable to regression formulation which effectively handles the problems associated with the long-tail distribution of the data. Additionally, we contribute the largest open-access dataset for suturing images and the first dataset pertaining to the micro-suturing task. We also demonstrate that the performance of the proposed algorithm surpasses the performance of human experts and also other state-of-the-art (SOTA) algorithms.