Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark.

ABSTRACT:

Purpose

This work presents FASTRL, a benchmark set of instrument manipulation tasks adapted to the domain of reinforcement learning and used in simulated surgical training. This benchmark enables and supports the design and training of human-centric reinforcement learning agents which assist and evaluate human trainees in surgical practice.

Methods

Simulation tasks from the Fundamentals of Arthroscopic Surgery Training (FAST) program are adapted to the reinforcement learning setting for the purpose of training virtual agents that are capable of providing assistance and scoring to the surgical trainees. A skill performance assessment protocol is presented based on the trained virtual agents.

Results

The proposed benchmark suite presents an API for training reinforcemen

SUBMITTER: Ovinnikov I 

PROVIDER: S-EPMC11365860 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature