Deep learning algorithms to assist in imaging diagnosis in individuals with disc herniation or spondylolisthesis: A scoping review.

BACKGROUND

Deep learning applications in medical imaging have advanced significantly, supporting the diagnosis of spinal disorders such as disc herniation and spondylolisthesis. This study aimed to review deep learning algorithms used in diagnostic imaging for these conditions.

METHODS

A scoping review was conducted following PRISMA-ScR guidelines and registered in the Open Science Framework. Literature searches were performed in PubMed, Lilacs, ScienceDirect, Web of Science, Wiley Online Library, Embase, IEEE Xplore, and Google Scholar. Studies published in the last ten years in English, Portuguese, or Spanish applying deep learning to lumbar spine imaging were included. Exclusions comprised reviews, expert opinions, and studies not focusing on lumbar imaging. Of 258 identified records, 71 duplicates were removed, leaving 187 for screening. After full-text assessment, 18 met eligibility criteria.

RESULTS

Nine studies investigated disc herniation, primarily using magnetic resonance imaging (MRI), while the remaining nine focused on spondylolisthesis based on X-ray imaging. Convolutional neural networks (CNNs), particularly ResNet-based architectures, were the most frequently used models, demonstrating high accuracy and sensitivity in classification tasks. MRI was predominant for disc herniation, while X-ray was preferred for spondylolisthesis. However, limitations included small dataset sizes, lack of external validation, and challenges in generalizing findings across populations.

CONCLUSION

While deep learning holds promise for enhancing diagnostic accuracy and efficiency, further research is needed to standardize evaluation methods, expand dataset diversity, and improve model robustness for real-world clinical applications.