Summary of key studies in deep learning for fracture analysis and non-union prediction
| Reference | Study focus | Main finding | Performance parameters |
|---|---|---|---|
| Porter et al. (2016) [13] | Automated measurement of fracture callus using portable software | Quantitatively monitored callus progression, indicating potential for early non-union detection | Improved measurement consistency |
| Kalmet et al. (2020) [19] | Deep learning for fracture detection | Demonstrated reliable fracture detection on radiographs | Enhanced sensitivity and specificity over traditional methods |
| Chung et al. (2018) [20] | Automated detection and classification of proximal humerus fractures | Achieved high accuracy in detecting and classifying proximal humerus fractures | Accuracy metrics comparable to expert assessments |
| Tanzi et al. (2020) [21] | X-ray bone fracture classification using deep learning | Established a baseline for fracture classification accuracy with potential for further improvement | Accuracy values in line with clinical expectations |
| Stojadinovic et al. (2011) [15] | Prognostic naïve Bayesian classifier for non-union prediction | Developed a Bayesian model to predict fracture healing outcomes following shock wave therapy | Specific performance metrics are not detailed |
| Degenhart et al. (2023) [18] | Computer-based mechanobiological fracture healing model for predicting non-union | Proposed a simulation model predicting healing outcomes after intramedullary nailing with promising results | Preliminary findings: detailed metrics require further validation |