A Machine Learning Approach to Discriminate Between Soft and Hard Bone Tissues Using Drilling Sounds
Bibliographic record
Abstract
Bone drilling is conducted in many surgical disciplines such as orthopedics, maxillofacial, and spine surgery. Most of these procedures involve drilling of different bone materials including hard (cortical) and soft (cancellous) tissues. Identifying these tissues is essential for surgeons to minimize damage to underlying nerves and vessels. The sound signal generated during drilling is a valuable source of information that could potentially be employed. Such sounds can be captured readily and easily through non-contact sensors. Therefore, our goal in this preliminary study is to investigate whether drilling sounds can enable us to distinguish between cortical and cancellous tissues. A bovine tibial bone was drilled, and the cortical and cancellous drilling sounds were captured. Each sound record was divided into small windows with a length of 50 ms and a 50% overlap. The window length was selected small, because our intended longer-term application is to provide the surgeon with near-real-time feedback. Short time Fourier Transform (STFT) coefficients were extracted from each window and were averaged accordingly to obtain p features. A support vector machine (SVM) algorithm was used for classification, and its accuracy was evaluated for different number of features (p). Two training/testing scenarios were considered, atlas (ATL) and leave-one-out (LOO). The total accuracies for ATL and LOO were 100% and 93.8% respectively obtained for p=128. Our study on a single specimen demonstrated that it is possible to discriminate between cortical and cancellous bones based on relatively short 50 ms windows of drilling sounds.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".