Automated Surgical Skill Assessment in Endoscopic Pituitary Surgery using Real-time Instrument Tracking on a High-fidelity Bench-top Phantom

TL;DR

The nasal phase of simulated endoscopic pituitary surgery is introduced: the nasal phase of simulated endoscopic pituitary surgery, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery.

Abstract

Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective; labour-intensive; and requires domain specific expertise. Automated data driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models in minimally invasive surgery. However, these models have been tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery. In this paper, a new public dataset is introduced, focusing on simulated surgery, using the nasal phase of endoscopic pituitary surgery as an exemplar. Simulated surgery allows for a realistic yet repeatable environment, meaning the insights gained from automated assessment can be used by novice surgeons to hone their skills on the simulator before moving to real surgery. PRINTNet (Pituitary Real-time INstrument Tracking Network) has been created as a baseline model for this automated assessment. Consisting of DeepLabV3 for classification and segmentation; StrongSORT for tracking; and the NVIDIA Holoscan SDK for real-time performance, PRINTNet achieved 71.9% Multiple Object Tracking Precision running at 22 Frames Per Second. Using this tracking output, a Multilayer Perceptron achieved 87% accuracy in predicting surgical skill level (novice or expert), with the "ratio of total procedure time to instrument visible time" correlated with higher surgical skill. This therefore demonstrates the feasibility of automated surgical skill assessment in simulated endoscopic pituitary surgery. The new publicly available dataset can be found here: https://doi.org/10.5522/04/26511049.

Figures (6)

• Figure 1: Representative images of the 4-instrument-classes used in the nasal phase of endoscopic pituitary surgery.
• Figure 2: Distribution of instruments: (a) Total number of images before data balancing; (b) Number of images per fold after data balancing.
• Figure 3: Complete workflow diagram of this study.
• Figure 4: Distribution of mOSATS (10-aspects, max 50) across the 15-videos.
• Figure 5: Qualitative results of PRINTNet: (a) a strong example where the classification, segmentation, and tracking are accurate; (b) a common example where the classification and tracking are accurate, but the segmentation could be improved at the instrument tip; (c) an uncommon example where classification, segmentation, and tracking are all inaccurate. (See the Supplementary Material for the full video.)