Augmented Reality in Neurosurgery, State of Art and Future Projections. A Systematic Review
Bibliographic record
Abstract
Background: The use of augmented reality (AR) is growing in medical education, in particular, in radiology and surgery. AR has the potential to become a strategic component of neurosurgical training courses. In fact, over the years, there has been a progressive increase in the application of AR in the various fields of neurosurgery. In this study, the authors aim to define the diffusion of these augmented reality systems in recent years. This study describes future trends in augmented reality for neurosurgeons. Methods: A systematic review of the literature was conducted to identify research published from December 1st, 2011 to November 30th, 2021. Electronic databases (PubMed, PubMed Central, and Scopus) were screened. The methodological quality of studies and extracted data were assessed for "augmented reality" and "neurosurgery". The data analysis focused on the geographical distribution, temporal evolution, and topic of augmented reality in neurosurgery. Results: A total of 198 studies have been included. The number of augmented reality applications in the neurosurgical field has increased during the last 10 years. The main topics on which it is mostly applied are spine surgery, neuronavigation, and education. The geographical distribution shows extensive use of augmented reality in the USA, Germany, China, and Canada. North America is the continent that uses augmented reality the most in the training and education of medical students, residents, and surgeons, besides giving the greatest research contribution in spine surgery, brain oncology, and surgical planning. AR is also extensively used in Asia for intraoperative navigation. Nevertheless, augmented reality is still far from reaching Africa and other countries with limited facilities, as no publications could be retrieved from our search. Conclusions: The use of AR is significantly increased in the last 10 years. Nowadays it is mainly used in spine surgery and for neurosurgical education, especially in North America, Europe and China. A continuous growth, also in other aspects of the specialty, is expected in the next future.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".