Mixed Reality in Modern Surgical and Interventional Practice: Narrative Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Mixed reality (MR) and its potential applications have gained increasing interest within the medical community over the recent years. The ability to integrate virtual objects into a real-world environment within a single video-see-through display is a topic that sparks imagination. Given these characteristics, MR could facilitate preoperative and preinterventional planning, provide intraoperative and intrainterventional guidance, and aid in education and training, thereby improving the skills and merits of surgeons and residents alike. OBJECTIVE: In this narrative review, we provide a broad overview of the different applications of MR within the entire spectrum of surgical and interventional practice and elucidate on potential future directions. METHODS: A targeted literature search within the PubMed, Embase, and Cochrane databases was performed regarding the application of MR within surgical and interventional practice. Studies were included if they met the criteria for technological readiness level 5, and as such, had to be validated in a relevant environment. RESULTS: A total of 57 studies were included and divided into studies regarding preoperative and interventional planning, intraoperative and interventional guidance, as well as training and education. CONCLUSIONS: The overall experience with MR is positive. The main benefits of MR seem to be related to improved efficiency. Limitations primarily seem to be related to constraints associated with head-mounted display. Future directions should be aimed at improving head-mounted display technology as well as incorporation of MR within surgical microscopes, robots, and design of trials to prove superiority.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".