RL-Based Guidance in Outpatient Hysteroscopy Training: A Feasibility Study

TL;DR

This study develops an RL-based agent for outpatient hysteroscopy training that autonomously performs cervical passage, demonstrating high success rates and outperforming clinicians in key metrics, thus enhancing training and procedural safety.

Contribution

The paper introduces a novel RL-based subsystem for autonomous cervical passage in hysteroscopy training, extending previous platforms and enabling virtual instruction and performance assessment.

Findings

The RL agent succeeded in 98% of trials.

It outperformed clinicians in three of four metrics.

The approach ensures smooth exploration and high reliability.

Abstract

This work presents an RL-based agent for outpatient hysteroscopy training. Hysteroscopy is a gynecological procedure for examination of the uterine cavity. Recent advancements enabled performing this type of intervention in the outpatient setup without anaesthesia. While being beneficial to the patient, this approach introduces new challenges for clinicians, who should take additional measures to maintain the level of patient comfort and prevent tissue damage. Our prior work has presented a platform for hysteroscopic training with the focus on the passage of the cervical canal. With this work, we aim to extend the functionality of the platform by designing a subsystem that autonomously performs the task of the passage of the cervical canal. This feature can later be used as a virtual instructor to provide educational cues for trainees and assess their performance. The developed algorithm is based on the soft actor critic approach to smooth the learning curve of the agent and ensure uniform exploration of the workspace. The designed algorithm was tested against the performance of five clinicians. Overall, the algorithm demonstrated high efficiency and reliability, succeeding in 98% of trials and outperforming the expert group in three out of four measured metrics.