Multicenter Assessment of Augmented Reality Registration Methods for Image-guided Interventions

Purpose: To evaluate manual and automatic registration times as well as accuracy with augmented reality during alignment of a holographic 3-dimensional (3D) model onto the real-world environment. Method: 18 participants in various stages of clinical training across two academic centers registered a 3D CT phantom model onto a CT grid using the HoloLens 2 augmented reality headset 3 consecutive times. Registration times and accuracy were compared among different registration methods (hand gesture, Xbox controller, and automatic registration), levels of clinical experience, and consecutive attempts. Registration times were also compared with prior HoloLens 1 data. Results: Mean aggregate manual registration times were 27.7, 24.3, and 72.8 seconds for one-handed gesture, two-handed gesture, and Xbox controller, respectively; mean automatic registration time was 5.3s (ANOVA p<0.0001). No significant difference in registration times was found among attendings, residents and fellows, and medical students (p>0.05). Significant improvements in registration times were detected across consecutive attempts using hand gestures (p<0.01). Compared with previously reported HoloLens 1 experience, hand gesture registration times were 81.7% faster (p<0.05). Registration accuracies were not significantly different across manual registration methods, measuring at 5.9, 9.5, and 8.6 mm with one-handed gesture, two-handed gesture, and Xbox controller, respectively (p>0.05). Conclusions: Manual registration times decreased significantly with updated hand gesture maneuvers on HoloLens 2 versus HoloLens 1, approaching the registration times of automatic registration and outperforming Xbox controller mediated registration. These results will encourage wider clinical integration of HoloLens 2 in procedural medical care.

A Modern Non-SQL Approach to Radiology-Centric Search Engine Design with Clinical Validation

Healthcare data is increasing in size at an unprecedented speed with much attention on big data analysis and Artificial Intelligence application for quality assurance, clinical training, severity triaging, and decision support. Radiology is well-suited for innovation given its intrinsically paired linguistic and visual data. Previous attempts to unlock this information goldmine were encumbered by heterogeneity of human language, proprietary search algorithms, and lack of medicine-specific search performance matrices. We present a de novo process of developing a document-based, secure, efficient, and accurate search engine in the context of Radiology. We assess our implementation of the search engine with comparison to pre-existing manually collected clinical databases used previously for clinical research projects in addition to computational performance benchmarks and survey feedback. By leveraging efficient database architecture, search capability, and clinical thinking, radiologists are at the forefront of harnessing the power of healthcare data.