  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.
