Usability Study of Augmented Reality Visualization Modalities on Localization Accuracy in the Head and Neck: Randomized Crossover Trial

JMIR Serious Games, vol. 14, pp. e75962

Abstract

Background: Augmented reality head-mounted displays could overcome the spatial dissociation between medical imaging and the surgical field, which may be particularly important in anatomically dense regions, such as the head and neck. Although many head-mounted displays offer markerless inside-out tracking at a fraction of the cost of navigation systems, their overlay accuracy with superimposition (SI) modality onto the surgical field remains limited. The virtual twin (VT), displaying holography adjacent to the surgical field, may offer a viable alternative. However, its performance is still unclear. Objective: This study aimed to compare the accuracy and efficiency of the two visualization modalities, SI and VT, for anatomical localization in the head and neck region. Methods: In a randomized crossover trial to compare two augmented reality visualization modalities (SI and VT), 38 participants used a HoloLens 2 to localize point, line-based, and volume-based anatomical structures on head phantoms. Their performance was evaluated with respect to accuracy, workload, time, and user experience. Results: SI achieved significantly better point localization accuracy than VT both in absolute (mean 14.4, SD 4.2 mm vs mean 15.8, SD 5.5 mm; P=.003) and relative accuracy (mean 3.4, SD 2.2 mm vs mean 6.0, SD 5.0 mm; P<.001). In line-based structures, accuracy was comparable between SI (average surface distance [ASD], mean 23.4, SD 4.1 mm; Hausdorff distance [HD], mean 31.5, SD 7.8 mm) and VT (ASD=mean 23.0, SD 4.5 mm; P=.51; HD=mean 31.0, SD 7.5 mm; P=.57). However, SI showed significantly higher deviation than VT in volume-based structure (ASD=mean 37.1, SD 13.8 mm vs mean 34.1, SD 14.2 mm; P=.01; HD=mean 52.0, SD 16.8 mm vs mean 49.1, SD 15.8 mm; P=.03). Participants were faster with SI (P=.02), while workload NASA-TLX (National Aeronautics and Space Administration Task Load Index) scores did not demonstrate a significant difference (P=.79). Conclusions: Given that SI did not clearly outperform VT under overlaid soft tissue and viewing challenges, VT remains a viable alternative in certain surgical scenarios where high accuracy is not required. Future research should focus on optimizing viewing angle guidance and the linkage between the anatomical target and the skin surface.

Authors 10

1. Gijs Luijten

   Graz University of Technology · Essen University Hospital

   Affiliation as printed

   Center for Virtual and Extended Reality in Medicine (ZvRM), Essen University Hospital, Essen, Germany

   Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Essen, Germany

   Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Graz, Austria

2. Christina Gsaxner

   Graz University of Technology · Essen University Hospital

   Affiliation as printed

   Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Essen, Germany

   Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Graz, Austria

3. Kim Grunert Aachen Department of Oral and Maxillofacial Surgery Institute of Medical Informatics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen, 52074, Germany, 49 2418088231

   Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany

4. Alexis Bader Aachen Department of Oral and Maxillofacial Surgery Institute of Medical Informatics

   University of Waterloo · Essen University Hospital · Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen, 52074, Germany, 49 2418088231

   Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

   Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Essen, Germany

   Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany

5. Frank Hölzle Aachen Department of Oral and Maxillofacial Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen, 52074, Germany, 49 2418088231

6. Rainer Röhrig Aachen Institute of Medical Informatics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany

7. Matias de la Fuente Aachen Chair of Medical Engineering

   RWTH Aachen University

   Affiliation as printed

   Chair of Medical Engineering, RWTH Aachen University, Aachen, Germany

8. Jan Egger

   Essen University Hospital

   Affiliation as printed

   Center for Virtual and Extended Reality in Medicine (ZvRM), Essen University Hospital, Essen, Germany

   Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Essen, Germany

9. Kunpeng Xie Aachen Department of Oral and Maxillofacial Surgery Institute of Medical Informatics

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen, 52074, Germany, 49 2418088231

   Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany

10. Behrus Puladi Aachen Department of Oral and Maxillofacial Surgery Institute of Medical Informatics

    Universitätsklinikum Aachen · RWTH Aachen University

    Affiliation as printed

    Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen, 52074, Germany, 49 2418088231

    Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany