Identifying Acute Thoracolumbar Vertebral Compression Fractures From Low-Quality Small-Sample X-Ray Images: A
Abstract:
Timely and accurate diagnosis of acute thoracolumbar vertebral compression fractures in X-ray images is critical for initiating prompt and effective treatment, preventing potential neurological damage and long-term disability. Recent advancements in artificial intelligence (AI) have significantly improved medical imaging analysis, providing sophisticated tools to assist clinicians in diagnosing acute thoracolumbar vertebral compression fractures. Nonetheless, detecting these fractures through imaging remains challenging due to the complex overlapping of bony structures in the thoracolumbar region, variability in fracture patterns, and often subtle nature of these injuries. Additionally, the limited availability and sometimes poor quality of medical images further complicate accurate AI-based detection. Addressing these challenges, this study introduces a transfer learning model optimized for recognizing acute thoracolumbar vertebral compression fractures from a small set of low-quality X-ray images. The model starts with a feature extraction model that analyzes multiple texture features of X-ray images. It then employs a Vision Transformer Detector (ViTDet) combined with a faster region-based convolutional neural network (Faster R-CNN) to recognize fractures efficiently. To enhance its performance on small datasets, the model employs a transfer learning approach for training. Extensive experiments with a large dataset of real-world images have shown that this model can effectively recognize acute thoracolumbar vertebral compression fractures from low-quality images, outperforming professionals with specialized knowledge in some cases.
