Deep Learning-Based Intelligent Diagnosis of Lumbar Diseases with Multi-Angle View of Intervertebral Disc

• Published in: Mathematics (Basel) Vol. 12; no. 13; p. 2062
• Main Authors: Chen, Kaisi (Kathy), Zheng, Lei, Zhao, Honghao, Wang, Zihang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Back pain; Classification; Deep learning; degenerative lumbar spine disease; Diagnosis; Disease; Image classification; Intervertebral discs; Machine learning; Magnetic resonance imaging; mask RCNN model; Medical imaging; multi-angle view of disc; Neural networks; Spine; two-staged classification; Vertebrae; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12132062

The diagnosis of degenerative lumbar spine disease mainly relies on clinical manifestations and imaging examinations. However, the clinical manifestations are sometimes not obvious, and the diagnosis of medical imaging is usually time-consuming and highly relies on the doctor’s personal experiences. Therefore, a smart diagnostic technology that can assist doctors in manual diagnosis has become particularly urgent. Taking advantage of the development of artificial intelligence, a series of solutions have been proposed for the diagnosis of spinal diseases by using deep learning methods. The proposed methods produce appealing results, but the majority of these approaches are based on sagittal and axial images separately, which limits the capability of different deep learning methods due to the insufficient use of data. In this article, we propose a two-stage classification process that fully utilizes image data. In particular, in the first stage, we used the Mask RCNN model to identify the lumbar spine in the spine image, locate the position of the vertebra and disc, and complete rough classification. In the fine classification stage, a multi-angle view of the intervertebral disc is generated by splicing the sagittal and axial slices of the intervertebral disc up and down based on the key position identified in the first stage, which provides more pieces of information to the deep learning methods for classification. The experimental results reveal substantial performance enhancements with the synthesized multi-angle view, achieving an F1 score of 96.67%. This represents a performance increase of approximately 15% over the sagittal images at 84.48% and nearly 14% over the axial images at 83.15%. This indicates that the proposed paradigm is feasible and more effective in identifying spinal-related degenerative diseases through medical images.