Augmented Reality-Based Surgery on the Human Cadaver Using a New Generation of Optical Head-Mounted Displays: Development and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Although nearly one-third of the world's disease burden requires surgical care, only a small proportion of digital health applications are directly used in the surgical field. In the coming decades, the application of augmented reality (AR) with a new generation of optical-see-through head-mounted displays (OST-HMDs) like the HoloLens (Microsoft Corp) has the potential to bring digital health into the surgical field. However, for the application to be performed on a living person, proof of performance must first be provided due to regulatory requirements. In this regard, cadaver studies could provide initial evidence. OBJECTIVE: The goal of the research was to develop an open-source system for AR-based surgery on human cadavers using freely available technologies. METHODS: We tested our system using an easy-to-understand scenario in which fractured zygomatic arches of the face had to be repositioned with visual and auditory feedback to the investigators using a HoloLens. Results were verified with postoperative imaging and assessed in a blinded fashion by 2 investigators. The developed system and scenario were qualitatively evaluated by consensus interview and individual questionnaires. RESULTS: The development and implementation of our system was feasible and could be realized in the course of a cadaver study. The AR system was found helpful by the investigators for spatial perception in addition to the combination of visual as well as auditory feedback. The surgical end point could be determined metrically as well as by assessment. CONCLUSIONS: The development and application of an AR-based surgical system using freely available technologies to perform OST-HMD-guided surgical procedures in cadavers is feasible. Cadaver studies are suitable for OST-HMD-guided interventions to measure a surgical end point and provide an initial data foundation for future clinical trials. The availability of free systems for researchers could be helpful for a possible translation process from digital health to AR-based surgery using OST-HMDs in the operating theater via cadaver studies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".