Augmented Reality in Vascular and Endovascular Surgery: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Technological advances have transformed vascular intervention in recent decades. In particular, improvements in imaging and data processing have allowed for the development of increasingly complex endovascular and hybrid interventions. Augmented reality (AR) is a subject of growing interest in surgery, with the potential to improve clinicians' understanding of 3D anatomy and aid in the processing of real-time information. This study hopes to elucidate the potential impact of AR technology in the rapidly evolving fields of vascular and endovascular surgery. OBJECTIVE: The aim of this review is to summarize the fundamental concepts of AR technologies and conduct a scoping review of the impact of AR and mixed reality in vascular and endovascular surgery. METHODS: A systematic search of MEDLINE, Scopus, and Embase was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. All studies written in English from inception until January 8, 2021, were included in the search. Combinations of the following keywords were used in the systematic search string: ("augmented reality" OR "hololens" OR "image overlay" OR "daqri" OR "magic leap" OR "immersive reality" OR "extended reality" OR "mixed reality" OR "head mounted display") AND ("vascular surgery" OR "endovascular"). Studies were selected through a blinded process between 2 investigators (JE and AS) and assessed using data quality tools. RESULTS: AR technologies have had a number of applications in vascular and endovascular surgery. Most studies (22/32, 69%) used 3D imaging of computed tomography angiogram-derived images of vascular anatomy to augment clinicians' anatomical understanding during procedures. A wide range of AR technologies were used, with heads up fusion imaging and AR head-mounted displays being the most commonly applied clinically. AR applications included guiding open, robotic, and endovascular surgery while minimizing dissection, improving procedural times, and reducing radiation and contrast exposure. CONCLUSIONS: AR has shown promising developments in the field of vascular and endovascular surgery, with potential benefits to surgeons and patients alike. These include reductions in patient risk and operating times as well as in contrast and radiation exposure for radiological interventions. Further technological advances are required to overcome current limitations, including processing capacity and vascular deformation by instrumentation.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".