iSurgARy: A mobile augmented reality solution for ventriculostomy in resource-limited settings

TL;DR

This paper introduces iSurgARy, a mobile augmented reality system designed to improve ventriculostomy accuracy in resource-limited settings, utilizing LiDaR-based landmark registration and validated through user studies involving novices and experts.

Contribution

The study presents a novel AR-based surgical guidance system tailored for low-resource environments, demonstrating its usability and accuracy through comprehensive user evaluations.

Findings

iSurgARy improved surgical accuracy as measured by RMSE

Participants rated iSurgARy highly on usability scales

The system reduced cognitive workload for users

Abstract

Global disparities in neurosurgical care necessitate innovations addressing affordability and accuracy, particularly for critical procedures like ventriculostomy. This intervention, vital for managing life-threatening intracranial pressure increases, is associated with catheter misplacement rates exceeding 30% when using a freehand technique. Such misplacements hold severe consequences including haemorrhage, infection, prolonged hospital stays, and even morbidity and mortality. To address this issue, we present a novel, stand-alone mobile-based augmented reality system (iSurgARy) aimed at significantly improving ventriculostomy accuracy, particularly in resource-limited settings such as those in low- and middle-income countries. iSurgARy uses landmark based registration by taking advantage of Light Detection and Ranging (LiDaR) to allow for accurate surgical guidance. To evaluate iSurgARy, we conducted a two-phase user study. Initially, we assessed usability and learnability with novice participants using the System Usability Scale (SUS), incorporating their feedback to refine the application. In the second phase, we engaged human-computer interaction (HCI) and clinical domain experts to evaluate our application, measuring Root Mean Square Error (RMSE), System Usability Scale (SUS) and NASA Task Load Index (TLX) metrics to assess accuracy usability, and cognitive workload, respectively