Using object detection for surgical tool recognition in simulated open inguinal hernia repair surgery
Bibliographic record
Abstract
Following the shift from time-based medical education to a competency-based approach, a computer-assisted training platform would help relieve some of the new time burden placed on physicians. A vital component of these platforms is the computation of competency metrics which are based on surgical tool motion. Recognizing the class and motion of surgical tools is one step in the development of a training platform. Object detection can achieve tool recognition. While previous literature has reported on tool recognition in minimally invasive surgeries, open surgeries have not received the same attention. Open Inguinal Hernia Repair (OIHR), a common surgery that general surgery residents must learn, is an example of such surgeries. We present a method for object detection to recognize surgical tools in simulated OIHR. Images were extracted from six video recordings of OIHR performed on phantoms. Tools were labelled with bounding boxes. A YOLOV3 object-detection model was trained to recognize the tools used in OIHR. The Average Precision scores per class and the mean Average Precision (mAP) were reported to benchmark the model’s performance. The mAP of the tool classes was 0.61, with individual Average Precision scores reaching up to 0.98. Tools with poor visibility or similar shapes such as the forceps, or scissors achieved lower precision scores. With an object detection network that can identify tools, research can be done on tissue-tool interactions to achieve workflow recognition. Workflow recognition would allow a training platform to detect the tasks performed in hernia repair surgeries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".