Automated Assessment of Kidney Ureteroscopy Exploration for Training

TL;DR

This paper introduces an automated, video-based system for assessing kidney ureteroscopy training in phantoms, providing accurate feedback without expert supervision, thus expanding training opportunities.

Contribution

It presents a novel ureteroscope video-based localization framework that automatically identifies missed calyces during phantom exploration, enabling autonomous training feedback.

Findings

Correctly classified 69 of 74 calyces in 15 videos

Achieved less than 4mm camera pose localization error

System processes a typical exploration in about 10 minutes

Abstract

Purpose: Kidney ureteroscopic navigation is challenging with a steep learning curve. However, current clinical training has major deficiencies, as it requires one-on-one feedback from experts and occurs in the operating room (OR). Therefore, there is a need for a phantom training system with automated feedback to greatly \revision{expand} training opportunities. Methods: We propose a novel, purely ureteroscope video-based scope localization framework that automatically identifies calyces missed by the trainee in a phantom kidney exploration. We use a slow, thorough, prior exploration video of the kidney to generate a reference reconstruction. Then, this reference reconstruction can be used to localize any exploration video of the same phantom. Results: In 15 exploration videos, a total of 69 out of 74 calyces were correctly classified. We achieve < 4mm camera pose localization error. Given the reference reconstruction, the system takes 10 minutes to generate the results for a typical exploration (1-2 minute long). Conclusion: We demonstrate a novel camera localization framework that can provide accurate and automatic feedback for kidney phantom explorations. We show its ability as a valid tool that enables out-of-OR training without requiring supervision from an expert.