A Comprehensive Survey on Deep Learning Methods for Automated Spinal Fracture Detection and Classification
Abstract
Trauma, osteoporosis, or metastatic diseases are the most prevalent causes of spinal fractures, a dangerous medical condition. These fractures have the potential to cause serious neurological problems, if they are misdiagnosed or misclassified, leading to diminished quality of life and persistent pain. Conventional diagnostic methods, including X-rays, computed tomography (CT), and magnetic resonance imaging (MRI), rely on expert interpretation by radiologists. However, manual assessment is time-consuming, subject to inter-observer variability, and may lead to diagnostic inconsistencies, particularly in subtle or complex cases. With advancements in artificial intelligence (AI), deep learning has revealed abundant possibility in automating spinal fracture detection and classification, improving both speed and accuracy. In recent years, deep learning techniques have developed as a dominant tool for automated spinal fracture detection and classification, offering great precision and effectiveness. This survey delivers a broad review of state-of-the-art deep learning models applied to spinal fracture analysis, covering CNNs, transformer-based architectures, and hybrid approaches. We analyze several publicly accessible datasets, preprocessing techniques, model architectures, and evaluation metrics used in the literature. The research gap is examined in the outcome section of this study. Finally, we outline future research directions, emphasizing the need for improved generalization, explainability, and integration with clinical workflows. This survey aims to serve as a useful reference for researchers and clinicians seeking to advance automated spinal fracture diagnosis using deep learning.
References
[2] S. Maki et al., “Machine Learning and Deep Learning in Spinal Injury Diagnosis”, J. Clin. Med., vol. 13, no. 705, pp. 1–16, Jan. 2024.
[3] J.S. Degadwala, V. N. Dasavandi Krishnamurthy, and D. Vyas, “Deep- Spine: Multi-Class Spine X-Ray Conditions Classification Using Deep Learning”, in Proceedings of the 3rd International Conference on Senti- ment Analysis and Deep Learning (ICSADL), 2024, pp. 1-6.
[4] M. N. Meadi, A. E. M. Zerari, and H. Benbrahim, “Cervical Spine Fracture Detection Using Deep Learning Algorithm”, in Proc. 8th Int. Conf. on Image and Signal Processing and Their Applications (ISPA), 2024.
[5] T. R. Arunkumar, V. Nagaral, and R. M. Ingaleshwar, “Bone Fracture Detection Using Machine Learning”, Int. Res. J. Mod. Eng. Technol. Sci., vol. 6, no. 8, pp. 2302–2306, Aug. 2024.
[6] M. Yaseen, M. Ali, S. Ali, A. Hussain, M.-I. Joo, and H.-C. Kim, “Cervical Spine Fracture Detection and Classification Using Two-Stage Deep Learning Methodology”, IEEE Access, vol. 12, pp. 72131–72135, May 2024.
[7] D. Chandrakala, G. M, I. K, and S. A, “A Deep Learning Approach on Cervical Spine Fracture Detection”, International Journal of Novel Research and Development (IJNRD), vol. 8, no. 6, Jun. 2023.
[8] J. Wu et al. “Convolutional Neural Network for Detecting Rib Fractures on Chest Radiographs: A Feasibility Study”, BMC Medical Imaging, vol. 23, pp. 1–12, 2023.
[9] N. Hong et al., “Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography”, J. Bone Miner. Res., vol. 38, pp. 887–895, Jun. 2023.
[10] S. M. Naguib et al., “Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map”, Diagnostics, vol. 13, pp. 1–16, Mar. 2023.
[11] P. Chład and M. R. Ogiela, “Deep Learning and Cloud-Based Computa- tion for Cervical Spine Fracture Detection System”, Electronics, vol. 12,
pp. 1–16, Apr. 2023.
[12] M. Lakshmana Kumar et al.,L, “everaging Deep Learning for Accurate Detection and Precise Localization of Vertebral Fractures in Medical Imaging”, in Proc. ICOSEC, pp. 826–832, 2023.
[13] P. S. et al., “Detection of Spinal Cord Injury Using Deep Learning Algorithm,” in Proc. ICSCDS, pp. 270–276, 2022.
[14] M. B. S. Bhavya et al., “Cervical Spine Fracture Detection Using PyTorch”, in Proc. ICMNWC, pp. 1–5, 2022.
[15] H.-Y. Chen et al., “Application of Deep Learning Algorithm to Detect and Visualize Vertebral Fractures on Plain Frontal Radiographs”, PLoS ONE, vol. 16, pp. 1–5, Jan. 2021.
[16] D. H. Kim, J. G. Jeong, Y. J. Kim, K. G. Kim, and J. Y. Jeon, “Automated Vertebral Segmentation and Measurement of Vertebral Com- pression Ratio Based on Deep Learning in X-Ray Images”, J. Digit. Imaging, vol. 34, pp. 853–861, Jul. 2021.
[17] G. Sha, J. Wu, and B. Yu, ”Detection of Spinal Fracture Lesions Based on Improved Faster-RCNN,” in Proc. IEEE Int. Conf. Artif. Intell. Inf. Syst. (ICAIIS), Dalian, China, pp. 29–32, Mar. 2020.
[18] K. C. Kim, H. S. Yun, S. Kim, and J. K. Seo, “Automation of Spine Curve Assessment in Frontal Radiographs Using Deep Learning of Vertebral-Tilt Vector”, IEEE Access, vol. 8, pp. 84618–84630, May 2020.
[19] J. E. Small, P. Osler, A. B. Paul, and M. Kunst, “CT Cervical Spine Fracture Detection Using a Convolutional Neural Network”, AJNR Am. J. Neuroradiol., vol. 42,pp. 1341–1347, Jul. 2021.
[20] S. W. Chung et al., “Automated Detection and Classification of the Proximal Humerus Fracture by Using Deep Learning Algorithm”, Acta Orthopaedica., vol. 89, pp. 468–473, Jul. 2018.
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