Precision Spine Care: A New Era of Discovery, Innovation, and Global Impact

Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review

BACKGROUND

Low back pain is the leading cause of disability worldwide with a significant socioeconomic burden; artificial intelligence (AI) has proved to have a great potential in supporting clinical decisions at each stage of the healthcare process. In this article, we have systematically reviewed the available literature on the applications of AI-based Decision Support Systems (DSS) in the clinical prevention and management of Low Back Pain (LBP) due to lumbar degenerative spine disorders.

METHODS

A systematic review of Pubmed and Scopus databases was performed according to the PRISMA statement. Studies reporting the application of DSS to support the prevention and/or management of LBP due to lumbar degenerative diseases were included. The QUADAS-2 tool was utilized to assess the risk of bias in the included studies. The area under the curve (AUC) and accuracy were assessed for each study.

RESULTS

Twenty five articles met the inclusion criteria. Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. The included studies mainly encompassed three tasks: clinical score definition, clinical assessment, and eligibility prediction and reached AUC scores of 0.93, 0.99 and 0.95, respectively.

CONCLUSIONS

AI-based DSS applications showed a high degree of accuracy in performing a wide set of different tasks. These findings lay the foundation for further research to improve the current understanding and encourage wider adoption of AI in clinical decision-making.

Why clinical trials in disc regeneration strive to achieve completion: Insights from publication status and funding sources

Background

Chronic discogenic low back pain (LBP) poses a significant global burden, yet effective therapeutic interventions directly targeting the underlying degenerative process remain elusive. After demonstrating promising results in preclinical studies, intradiscal injection of cell-based treatments has been increasingly investigated in the clinical setting. However, most clinical trials failed to reach publication, with the few available reports showing only minor improvements. The aim of this study was to analyze the prospective clinical trials registered on ClinicalTrials.gov investigating cell therapies for LBP, with a specific emphasis on identifying critical obstacles hindering study completion, including trial design and funding sources.

Methods

A systematic search of prospective clinical trials investigating cell-based treatments for chronic LBP due to intervertebral disc degeneration was performed on ClinicalTrials.gov. Extracted data encompassed study design, recruitment, experimental treatment modalities, investigated outcomes, current status, completion date, publication status, and funding sources. Fisher's exact test assessed associations between categorical variables, while a multiple logistic regression model aimed to identify factors potentially linked to the publication status of the studies.

Results

Our search identified 26 clinical trials. Among these, only 7 (26.9%) were published, and none of the other studies marked as completed reported any results on ClinicalTrials.gov. Fifty percent of included trials were funded by universities, whereas the rest was sponsored by industry (38.5%) or private institutions (11.5%). Experimental treatments primarily involved cell-based or cell-derived products of varying sources and concentrations. Products containing carriers, such as hyaluronic acid or fibrin, were more frequently funded by industry and private organizations (p = 0.0112). No significant differences emerged when comparing published and nonpublished studies based on funding, as well as between publication status and other variables.

Conclusion

Most clinical trials exploring cell-based disc regenerative therapies for chronic LBP have never reached completion, with only a small fraction reporting preliminary data in publications.

Discovery of circulating blood biomarkers in patients with and without Modic changes of the lumbar spine: a preliminary analysis

PURPOSE

The following study aimed to determine the existence of blood biomarkers in symptomatic patients with or without lumbar Modic changes (MC).

METHODS

A cross-sectional sub-analyses of a prospective cohort was performed. Fasting blood samples were collected from patients with and without lumbar MC who had undergone spinal fusion or microdiscectomy. An 80-plex panel and CCL5/RANTES were used to assess preoperative plasma cytokine concentrations. Patient demographics and imaging phenotypes were also assessed.

RESULTS

Thirty-one subjects were analysed (n = 18 no MC; n = 13 MC). No significant differences were found in age, sex, body mass index, smoking and alcohol history, and surgical procedure (i.e. fusion, decompression) between the two groups (p > 0.05). Several statistically significant blood biomarkers in MC patients were identified, including elevated levels of C-C Motif Chemokine Ligand 5 (CCL5, p = 0.0006), while Macrophage Migration Inhibitory Factor (MIF) was significantly lower (p = 0.009). Additionally, C-X-C Motif Chemokine Ligand 5 (CXCL5, p = 0.052), Pentraxin 3 (PTX3, p = 0.06) and Galectin-3 (Gal-3, p = 0.07) showed potential relevance. Moreover, MC patients exhibited significantly higher levels of disc degeneration (p = 0.0001) and displacement severity (p = 0.020). Based on multivariate analyses and controlling for disc degeneration/displacement, CCL5 (OR 1.02; 95% CI 1.002-1.033; p = 0.028) and MIF (OR 0.60; 95% CI 0.382-0.951; p = 0.030) were independently associated with MC patients.

CONCLUSION

This "proof-of-concept" study is the first to identify specific and significantly circulating blood biomarkers associated with symptomatic patients with lumbar MC, independent of disc alterations of degeneration and/or bulges/herniations. Specifically, differences in CCL5 and MIF protein levels were significantly noted in MC patients compared to those without MC.

Artificial intelligence in spine care: current applications and future utility

PURPOSE

The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

METHODS

A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

RESULTS

This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

CONCLUSION

Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion

PURPOSE

Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF.

METHODS

Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance.

RESULTS

In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features.

CONCLUSIONS

Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.

Mechanisms and clinical implications of intervertebral disc calcification

Low back pain is a leading cause of disability worldwide. Intervertebral disc (IVD) degeneration is often associated with low back pain but is sometimes asymptomatic. IVD calcification is an often overlooked disc phenotype that might have considerable clinical impact. IVD calcification is not a rare finding in ageing or in degenerative and scoliotic spinal conditions, but is often ignored and under-reported. IVD calcification may lead to stiffer IVDs and altered segmental biomechanics, more severe IVD degeneration, inflammation and low back pain. Calcification is not restricted to the IVD but is also observed in the degeneration of other cartilaginous tissues, such as joint cartilage, and is involved in the tissue inflammatory process. Furthermore, IVD calcification may also affect the vertebral endplate, leading to Modic changes (non-neoplastic subchondral vertebral bone marrow lesions) and the generation of pain. Such effects in the spine might develop in similar ways to the development of subchondral marrow lesions of the knee, which are associated with osteoarthritis-related pain. We propose that IVD calcification is a phenotypic biomarker of clinically relevant disc degeneration and endplate changes. As IVD calcification has implications for the management and prognosis of degenerative spinal changes and could affect targeted therapeutics and regenerative approaches for the spine, awareness of IVD calcification should be raised in the spine community.

Recent advances and new discoveries in the pipeline of the treatment of primary spinal tumors and spinal metastases: a scoping review of registered clinical studies from 2000 to 2020

The field of spinal oncology has substantially evolved over the past decades. This review synthesizes and appraises what was learned and what will potentially be discovered from the recently completed and ongoing clinical studies related to the treatment of primary and secondary spinal neoplasms. This scoping review included all clinical studies on the treatment of spinal neoplasms registered in the ClinicalTrials.gov website from February 2000 to December 2020. The terms "spinal cord tumor," "spinal metastasis," and "metastatic spinal cord compression" were used. Of the 174 registered clinical studies on primary spinal tumors and spinal metastasis, most of the clinical studies registered in this American registry were interventional studies led by single institutions in North America (n = 101), Europe (n = 43), Asia (n = 24), or other continents (n = 6). The registered clinical studies mainly focused on treatment strategies for spinal neoplasms (90.2%) that included investigating stereotactic radiosurgery (n = 33), radiotherapy (n = 21), chemotherapy (n = 20), and surgical technique (n = 11). Of the 69 completed studies, the results from 44 studies were published in the literature. In conclusion, this review highlights the key features of the 174 clinical studies on spinal neoplasms that were registered from 2000 to 2020. Clinical trials were heavily skewed toward the metastatic population as opposed to the primary tumors which likely reflects the rarity of the latter condition and associated challenges in undertaking prospective clinical studies in this population. This review serves to emphasize the need for a focused approach to enhancing translational research in spinal neoplasms with a particular emphasis on primary tumors.

Development of a standardized histopathology scoring system for human intervertebral disc degeneration: an Orthopaedic Research Society Spine Section Initiative

BACKGROUND

Histopathological analysis of intervertebral disc (IVD) tissues is a critical domain of back pain research. Identification, description, and classification of attributes that distinguish abnormal tissues form a basis for probing disease mechanisms and conceiving novel therapies. Unfortunately, lack of standardized methods and nomenclature can limit comparisons of results across studies and prevent organizing information into a clear representation of the hierarchical, spatial, and temporal patterns of IVD degeneration. Thus, the following Orthopaedic Research Society (ORS) Spine Section Initiative aimed to develop a standardized histopathology scoring scheme for human IVD degeneration.

METHODS

Guided by a working group of experts, this prospective process entailed a series of stages that consisted of reviewing and assessing past grading schemes, surveying IVD researchers globally on current practice and recommendations for a new grading system, utilizing expert opinion a taxonomy of histological grading was developed, and validation performed.

RESULTS

A standardized taxonomy was developed, which showed excellent intra-rater reliability for scoring nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous end plate (CEP) regions (interclass correlation [ICC] > .89). The ability to reliably detect subtle changes varied by IVD region, being poorest in the NP (ICC: .89-.95) where changes at the cellular level were important, vs the AF (ICC: .93-.98), CEP (ICC: .97-.98), and boney end plate (ICC: .96-.99) where matrix and structural changes varied more dramatically with degeneration.

CONCLUSIONS

The proposed grading system incorporates more comprehensive descriptions of degenerative features for all the IVD sub-tissues than prior criteria. While there was excellent reliability, our results reinforce the need for improved training, particularly for novice raters. Future evaluation of the proposed system in real-world settings (eg, at the microscope) will be needed to further refine criteria and more fully evaluate utility. This improved taxonomy could aid in the understanding of IVD degeneration phenotypes and their association with back pain.