A Serious Game to Study Reduced Field of View in Keyhole Surgery: Development and Experimental Study
Bibliographic record
Abstract
BACKGROUND: During keyhole surgery, the surgeon is required to perform highly demanding tasks while only being able to see part of the patient's anatomy. This limited field of view is widely cited as a key limitation of the procedure, and many computational methods have been proposed to overcome it. However, the precise effects of a limited field of view on task performance remain unknown due to the lack of tools to study these effects effectively. OBJECTIVE: This paper describes our work on developing a serious game with 2 objectives: (1) to create an engaging game that communicates some of the challenges of keyhole surgery, and (2) to test the effect of a limited field of view on task performance. The development of a serious game that can be played by a wide range of participants will enable us to gather quantitative data on the effects of the reduced field of view on task performance. These data can inform the future development of technologies to help surgeons reduce the impact of a limited field of view on clinical outcomes for patients. The game is open source and may be adapted and used by other researchers to study related problems. METHODS: We implemented an open-source serious game in JavaScript, inspired by the surgical task of selectively cauterizing blood vessels during twin-to-twin transfusion surgery. During the game, the player is required to identify and cut the correct blood vessel under different fields of view and varying levels of vascular complexity. We conducted a quantitative analysis of task performance time under different conditions and a formative analysis of the game using participant questionnaires. RESULTS: We recruited 25 players to test the game and recorded their task performance time, accuracy, and qualitative metrics. Reducing the field of view resulted in participants taking significantly longer (P<.001) to perform otherwise identical tasks (mean 6.4 seconds, 95% CI 5.0-7.8 seconds vs mean 13.6 seconds, 95% CI 10.3-16.9 seconds). Participants found the game engaging and agreed that it enhanced their understanding of the limited field of view during keyhole surgery. CONCLUSIONS: We recruited 25 players to test the game and recorded their task performance time, accuracy, and qualitative metrics. Reducing the field of view resulted in participants taking statistically significantly longer (16.4 vs 9.8 seconds; P=.05) to perform otherwise identical tasks. Participants found the game engaging and agreed that it enhanced their understanding of the limited field of view during keyhole surgery.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".