Augmenting Performance: A Systematic Review of Optical See-Through Head-Mounted Displays in Surgery.
Mitchell DoughtyNilesh R GhugreGraham A WrightPublished in: Journal of imaging (2022)
We conducted a systematic review of recent literature to understand the current challenges in the use of optical see-through head-mounted displays (OST-HMDs) for augmented reality (AR) assisted surgery. Using Google Scholar, 57 relevant articles from 1 January 2021 through 18 March 2022 were identified. Selected articles were then categorized based on a taxonomy that described the required components of an effective AR-based navigation system: data, processing, overlay, view, and validation. Our findings indicated a focus on orthopedic (n=20) and maxillofacial surgeries (n=8). For preoperative input data, computed tomography (CT) (n=34), and surface rendered models (n=39) were most commonly used to represent image information. Virtual content was commonly directly superimposed with the target site (n=47); this was achieved by surface tracking of fiducials (n=30), external tracking (n=16), or manual placement (n=11). Microsoft HoloLens devices (n=24 in 2021, n=7 in 2022) were the most frequently used OST-HMDs; gestures and/or voice (n=32) served as the preferred interaction paradigm. Though promising system accuracy in the order of 2-5 mm has been demonstrated in phantom models, several human factors and technical challenges-perception, ease of use, context, interaction, and occlusion-remain to be addressed prior to widespread adoption of OST-HMD led surgical navigation.
Keyphrases
- computed tomography
- electronic health record
- minimally invasive
- image quality
- dual energy
- coronary artery bypass
- high resolution
- endothelial cells
- positron emission tomography
- high speed
- optic nerve
- big data
- magnetic resonance imaging
- contrast enhanced
- systematic review
- deep learning
- patients undergoing
- induced pluripotent stem cells
- ultrasound guided
- virtual reality
- health information
- mass spectrometry
- healthcare
- machine learning
- coronary artery disease
- social media
- magnetic resonance