A Review on the Use of Artificial Intelligence in Spinal Diseases

Abstract

Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.

Fatty infiltration in cervical flexors and extensors in patients with degenerative cervical myelopathy using a multi-muscle segmentation model

“…Recent advances in artificial intelligence techniques have led to development of automated tools and application of machine learning algorithms in the field of spinal imaging [ 27 , 66 , 67 ]. In this study, we utilize a recently developed CNN model that segments seven bilateral cervical muscle groups (14 muscles in total) and measures muscle composition in each muscle group (Weber et.…”

A narrative review of machine learning as promising revolution in clinical practice of scoliosis

“…The comparison of different neural network algorithms could assist in selecting the best model with the best performance. Additionally, the advantages and disadvantages of different algorithms for each spinal disorder should also be considered (76). Therefore, future study should focus on the validation of ML models on heterogeneous test sets prior to deployment and the regulation of ML performance after deployment in clinical practice.…”

“…Thirdly, concept shifting from human vs. machine to human-and-machine may be essential to over these barriers. Take an example, there were four studies concluded that ML combining with clinical decision making is superior to ML models or clinical decision making alone (72,(76)(77)(78). Using the experience of clinicians as a prerequirement, the application of ML in spine deformity, such as scoliosis, will be more promising in increasing the accessibility of clinical data.…”

AI MSK clinical applications: spine imaging

“…Therefore, automated detection of frequent pathologies, especially in common projection radiography, will help maintaining efficient patient management in hospitals without constant radiologic services. A series of investigations has been published with focus on diagnosis of spinal disorders in the various radiologic modalities [46,47]. A recent systematic review by Azimi et al has identified more than 40 studies that implemented NNs for diagnosis of spine disorders.…”