Augmented reality visualisation for orthopaedic surgical guidance with pre‐ and intra‐operative multimodal image data fusion
Bibliographic record
Abstract
Augmented reality (AR) has proven to be a useful, exciting technology in several areas of healthcare. AR may especially enhance the operator's experience in minimally invasive surgical applications by providing more intuitive and naturally immersive visualisation in those procedures which heavily rely on three‐dimensional (3D) imaging data. Benefits include improved operator ergonomics, reduced fatigue, and simplified hand–eye coordination. Head‐mounted AR displays may hold great potential for enhancing surgical navigation given their compactness and intuitiveness of use. In this work, the authors propose a method that can intra‐operatively locate bone structures using tracked ultrasound (US), registers to the corresponding pre‐operative computed tomography (CT) data and generates 3D AR visualisation of the operated surgical scene through a head‐mounted display. The proposed method deploys optically‐tracked US, bone surface segmentation from the US and CT image volumes, and multimodal volume registration to align pre‐operative to the corresponding intra‐operative data. The enhanced surgical scene is then visualised in an AR framework using a HoloLens. They demonstrate the method's utility using a foam pelvis phantom and quantitatively assess accuracy by comparing the locations of fiducial markers in the real and virtual spaces, yielding root mean square errors of 3.22, 22.46, and 28.30 mm in the x , y , and z directions, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".