The continuous progress of research and clinical trials has offered a wide variety of information concerning the spine and the treatment of the different spinal pathologies that may occur. Planning the best therapy for each patient could be a very difficult and challenging task as it often requires thorough processing of the patient's history and individual characteristics by the clinician. Clinicians and researchers also face problems when it comes to data availability due to patients' personal information protection policies. Artificial intelligence refers to the reproduction of human intelligence via special programs and computers that are trained in a way that simulates human cognitive functions. Artificial intelligence implementations to daily clinical practice such as surgical robots that facilitate spine surgery and reduce radiation dosage to medical staff, special algorithms that can predict the possible outcomes of conservative versus surgical treatment in patients with low back pain and disk herniations, and systems that create artificial populations with great resemblance and similar characteristics to real patients are considered to be a novel breakthrough in modern medicine. To enhance the body of the related literature and inform the readers on the clinical applications of artificial intelligence, we performed this review to discuss the contribution of artificial intelligence in spine surgery and pathology.

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

MRI was used to measure the changes in the angle of the facet joints of the lumbar spine and analyze the relationship between it and the herniated lumbar intervertebral disc. Analysis of the causes of lumbar disc herniation from the anatomy and morphology of the spine provides a basis for the early diagnosis and prevention of lumbar disc herniation. There is a certain correlation between the changes shown in MRI imaging of lumbar disc herniation and the TCM syndromes of lumbar intervertebral disc herniation. There is a correlation between the syndromes of lumbar disc herniation and the direct signs of MRI: pathological type, herniated position, and degree of herniation. Indirect signs with MR, nerve root compression and dural sac compression, are related. The MRI examination results can help syndrome differentiation to improve its accuracy to a certain extent. MRI has high sensitivity for the measurement of the angle of the facet joints of the lumbar spine and can be used to study the correlation between the changes of the facet joint angles and the herniated disc. Facet joint asymmetry is closely related to lateral lumbar disc herniation, which may be one of its pathogenesis factors. The herniated intervertebral disc is mostly on the sagittal side of the facet joint, and the facet joint angle on the side of the herniated disc is more sagittal. The asymmetry of the facet joints is not related to the central lumbar disc herniation, and the angle of the facet joints on both sides of the central lumbar disc herniation is partial sagittal.

It has been reported that long intergenic non-protein-coding RNA 324 (LINC00324) promotes liver cancer by upregulating Fas ligand (FasL), which is a major player in intervertebral disk degeneration (IDD), indicating the involvement of LINC00324 in IDD. This study was carried out to investigate the interaction between LINC00324 and FasL in IDD.

Plasma samples were collected from both IDD (n = 60) and healthy controls (n = 60). The expression of LINC00324 and FasL in plasma was determined by RT-qPCR. The interactions between LINC00324 and FasL in nucleus pulposus (NP) cells were analyzed by overexpression experiments.

LINC00324 and FasL were upregulated in IDD patients, and they were positively correlated. After treatment, the expression levels of FasL and LINC00324 were significantly decreased. In NP cells, overexpression of LINC00324 increased the expression of FasL at both mRNA and protein levels, while overexpression of FasL did not affect the expression of LINC00324.

LINC00324 may upregulate FasL in IDD to promote disease progression.