Classifying hard and soft bone tissues using drilling sounds
Bibliographic record
Abstract
The purpose of this study was to investigate if the sounds generated during bone drilling could be used to classify between hard (cortical) and soft (cancellous) tissues. Bone drilling is performed in many surgical procedures throughout the world. Inadvertent deviation from the correct drill direction may result in injuries to sensitive anatomical structures such as nerve and vessels. Therefore, to increase the safety of such procedures, it is necessary to identify different bone tissues. The cortical and cancellous tissues of six bovine tibia pieces were drilled and the generated sounds were recorded. Each record was analyzed in different frequency regions based on the spectrograms. From each region, short-time Fourier transform (STFT) coefficients were computed and averaged accordingly to obtain n bins. The total bins of all frequency regions were chosen as the features. A support vector machine (SVM) algorithm was selected for classification and the performance was evaluated in two training/testing scenarios: leave one bone out (LOBO) and bone specific (BSP). The average total accuracy on the testing data was 70.9% and 83% for LOBO and BSP respectively. The results indicated that the drilling sounds obtained from various bone pieces could be used to develop a classification model that had promising performance on identifying hard and soft components of a new bone piece.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".