Accuracy and efficiency of drilling trajectories with augmented reality versus conventional navigation randomized crossover trial

Conventional navigation systems (CNS) in surgery require strong spatial cognitive abilities and hand-eye coordination. Augmented Reality Navigation Systems (ARNS) provide 3D guidance and may overcome these challenges, but their accuracy and efficiency compared to CNS have not been systematically eva...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:NPJ digital medicine 2024-11, Vol.7 (1), p.316-13, Article 316
Hauptverfasser: Li, Yao, Drobinsky, Sergey, Becker, Paulina, Xie, Kunpeng, Lipprandt, Myriam, Mueller, Christian Andreas, Egger, Jan, Hölzle, Frank, Röhrig, Rainer, Radermacher, Klaus, de la Fuente, Matías, Puladi, Behrus
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Conventional navigation systems (CNS) in surgery require strong spatial cognitive abilities and hand-eye coordination. Augmented Reality Navigation Systems (ARNS) provide 3D guidance and may overcome these challenges, but their accuracy and efficiency compared to CNS have not been systematically evaluated. In this randomized crossover study with 36 participants from different professional backgrounds (surgeons, students, engineers), drilling accuracy, time and perceived workload were evaluated using ARNS and CNS. For the first time, this study provides compelling evidence that ARNS and CNS have comparable accuracy in translational error. Differences in angle and depth error with ARNS were likely due to limited stereoscopic vision, hardware limitations, and design. Despite this, ARNS was preferred by most participants, including surgeons with prior navigation experience, and demonstrated a significantly better overall user experience. Depending on accuracy requirements, ARNS could serve as a viable alternative to CNS for guided drilling, with potential for future optimization.
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-024-01314-2