An Automated Machine Learning Framework for Surgical Suturing Action Detection under Class Imbalance

TL;DR

This paper introduces an automated machine learning framework for real-time detection of surgical actions in laparoscopic training, effectively handling class imbalance and enhancing interpretability for medical applications.

Contribution

It presents a rapid deployment approach using automated machine learning that addresses class imbalance and incorporates model transparency in surgical action detection.

Findings

Effective handling of class imbalance in surgical data

Traditional ML models outperform deep learning in deployment speed and interpretability

Demonstrated reliable real-time detection in surgical training environments

Abstract

In laparoscopy surgical training and evaluation, real-time detection of surgical actions with interpretable outputs is crucial for automated and real-time instructional feedback and skill development. Such capability would enable development of machine guided training systems. This paper presents a rapid deployment approach utilizing automated machine learning methods, based on surgical action data collected from both experienced and trainee surgeons. The proposed approach effectively tackles the challenge of highly imbalanced class distributions, ensuring robust predictions across varying skill levels of surgeons. Additionally, our method partially incorporates model transparency, addressing the reliability requirements in medical applications. Compared to deep learning approaches, traditional machine learning models not only facilitate efficient rapid deployment but also offer significant advantages in interpretability. Through experiments, this study demonstrates the potential of this approach to provide quick, reliable and effective real-time detection in surgical training environments