Comprehensive Summary
This systematic review evaluates the application of artificial intelligence (AI) and machine learning (ML) in diagnosing and classifying hip fractures. The review synthesizes data from 14 studies that utilized imaging modalities such as X-ray, CT, and MRI. The diagnostic accuracy of AI alone ranged from 79.3% to 98%, with a mean accuracy of 92% and an area under the curve (AUC) of 0.969. When AI assisted clinicians, accuracy improved to 97.1%. Fracture classification showed an average accuracy of 91.4% and an AUC of 0.933. Various convolutional neural network (CNN) architectures, including DenseNet and GoogLeNet, were employed. Data augmentation techniques expanded datasets significantly. The studies focused on femoral neck and intertrochanteric fractures, with some aligning to AO/OTA classification systems. Tools like Grad-CAM were used to enhance region-of-interest identification. The review highlights AI's potential to improve diagnostic accuracy and efficiency, especially in emergency settings.
Outcomes and Implications
The integration of AI in hip fracture diagnosis and classification holds significant clinical relevance. By improving diagnostic accuracy and efficiency, AI can reduce the rate of misdiagnosis, which currently ranges from 2% to 10% in emergency settings. This reduction can lead to fewer complications and lower mortality rates. AI's ability to quickly identify occult fractures, even without CT or MRI, is particularly beneficial in resource-limited environments. However, limitations such as exclusion of implant cases and lack of integration with clinical data need addressing. The technology shows promise in augmenting, but not replacing, human expertise in orthopedic diagnostics.