P.139 Predicting pituitary gland location during endoscopic endonasal surgery using machine learning model
Bibliographic record
Abstract
Background: Identifying the pituitary gland during surgery for pituitary neuroendocrine tumors (PitNET) is crucial for preserving gland tissue and reducing postoperative hormonal dysfunction. This study aimed to develop and validate a machine learning (ML) tool to identify the pituitary gland during endoscopic endonasal surgery. Methods: Anonymized surgical videos from PitNET resections were trimmed to key phases, starting after dura opening and ending before skull base reconstruction. Frames were manually annotated to delineate the pituitary gland’s location. The ML model’s performance was evaluated using a single hold-out set method. Results: A total of 2316 frames from 52 videos were annotated, with 60%, 20%, and 20% allocated to training, validating, and testing the ML model, respectively. Performance metrics were as follows: accuracy of 97.8%, specificity of 98.7%, recall of 27%, precision of 18.6%, and an F1-score of 0.22. Conclusions: This study highlights the feasibility of using ML to identify the pituitary gland in PitNET surgeries. While the model is highly accurate in distinguishing gland from non-gland tissue, its low precision indicates a propensity to misclassify adjacent background tissue as pituitary gland.Further refinements could enhance its precision, making it a valuable tool for improving intraoperative anatomical recognition and postoperative hormonal outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".