Skilled surgeons can detect subtle changes in bone-cutting sounds to recognize bone penetration, but acoustic cues are subjective and dependent on surgical experience. The objective of this study was to develop an artificial intelligence (AI) model capable of detecting bone penetration from intraoperative percussion sounds. A total of 1,236 chisel strikes were identified from intraoperative recordings obtained during lumbar and thoracic spinal decompression surgeries performed using a chisel, and were labeled as penetration or non-penetration. Acoustic features were extracted per strike and expanded across 3-hit sliding windows to capture dynamic temporal changes. A gradient boosting machine learning classifier (LightGBM) was trained, and 10% of the data were reserved as an independent test set. Model performance was primarily evaluated using the area under the precision–recall curve (PR-AUC) and receiver operating characteristic AUC (ROC-AUC). On the independent test set, the model yielded a ROC-AUC of 0.838 and a PR-AUC of 0.604. In a sensitivity analysis, the model achieved a sensitivity of 0.828, specificity of 0.767, accuracy 0.784, F1-score 0.686, and precision 0.585. Feature importance analysis revealed that dynamic changes between consecutive strikes, such as mel-frequency cepstral coefficients (MFCCs), zero-crossing rate, and spectral contrast, were the most influential predictors. An AI model analyzing intraoperative percussion sounds demonstrated a robust ability to detect bone penetration, with consistent ROC performance and clinically acceptable PR-AUC. Quantifying acoustic cues traditionally interpreted subjectively by surgeons may support intraoperative decision-making and surgical training.
Fujiwara et al. (Sun,) studied this question.