A Review on the Use of Artificial Intelligence in Spinal Diseases

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.

Cut-off Value for Body Mass Index in Predicting Surgical Success in Patients with Lumbar Spinal Canal Stenosis

STUDY DESIGN: Case-control. PURPOSE: To determine optimal cut-off value for body mass index (BMI) in predicting surgical success in patients with lumbar spinal canal stenosis (LSCS). OVERVIEW OF LITERATURE: BMI is an essential variable in the assessment of patients with LSCS. METHODS: We conducted a prospective study with obese and non-obese LSCS surgical patients and analyzed data on age, sex, duration of symptoms, walking distance, morphologic grade of stenosis, BMI, postoperative complications, and functional disability. Obesity was defined as BMI of ≥30 kg/m². Patients completed the Oswestry Disability Index (ODI) questionnaire before surgery and 2 years after surgery. Surgical success was defined as ≥30% improvement from the baseline ODI score. Receiver operating characteristic (ROC) analysis was used to estimate the optimal cut-off values of BMI to predict surgical success. In addition, correlation was assessed between BMI and stenosis grade based on morphology as defined by Schizas and colleague in total, 189 patients were eligible to enter the study. RESULTS: Mean age of patients was 61.5±9.6 years. Mean follow-up was 36±12 months. Most patients (88.4%) were classified with grades C (severe stenosis) and D (extreme stenosis). Post-surgical success was 85.7% at the 2-year follow-up. A weak correlation was observed between morphologic grade of stenosis and BMI. Rates of postoperative complications were similar between patients who were obese and those who were non-obese. Both cohorts had similar degree of improvement in the ODI at the 2-year follow-up. However, patients who were non-obese presented significantly higher surgical success than those who were obese. In ROC curve analysis, a cut-off value of ≤29.1 kg/m² for BMI in patients with LSCS was suggestive of surgical success, with 81.1% sensitivity and 82.2% specificity (area under the curve, 0.857; 95% confidence interval, 0.788–0.927). CONCLUSION: This study showed that the BMI can be considered a parameter for predicting surgical success in patients with LSCS and can be useful in clinical practice.

Validation of the Iranian Version of the ECOS-16 Questionnaire in Patients with Osteoporotic Vertebral Fractures

STUDY DESIGN: Prospective clinical study. PURPOSE: To translate and validate the Quality of Life Questionnaire of the European Foundation for Osteoporosis (ECOS-16) in patients with osteoporotic vertebral fractures in Iran. OVERVIEW OF LITERATURE: It is important to assess the psychometric properties of instruments measuring patient-reported outcomes. METHODS: The translation was performed using the backward-forward translation method. The final version was generated by consensus among the translators. Every woman who had a T-score of 0.70 for all scales. Test-retest reliability as indicated by intraclass correlation coefficient was found to be 0.85 (0.68–0.91). Additionally, the correlation of each item with its hypothesized domain of the ECOS-16 showed acceptable results, suggesting that the items had a substantial relationship with their own domains. Further analysis also indicated that the questionnaire was responsive to change (effect size, 0.85; standardized response mean, 0.93) (p<0.001). Significant correlations existed between scores of similar subscales of ECOS-16 and SF-36 (p<0.001). CONCLUSIONS: ECOS-16 is an acceptable, reliable, valid, and responsive measure to assess the quality of life in patients with osteoporotic vertebral fractures.

Preliminary Results of Relationship between Preoperative Walking Ability and Magnetic Resonance Imaging Morphology in Patients with Lumbar Canal Stenosis: Comparison between Trefoil and Triangle Types of Spinal Stenosis

STUDY DESIGN: Cross-sectional. PURPOSE: To examine the relationship between magnetic resonance imaging (MRI) morphology stenosis grades and preoperative walking ability in patients with lumbar canal stenosis (LCS). OVERVIEW OF LITERATURE: No previous study has analyzed the correlation between MRI morphology stenosis grades and walking ability in patients with LCS. METHODS: This prospective study included 98 consecutive patients with LCS who were candidates for surgery. Using features identified in T2-weighted axial magnetic, stenosis type was determined at the maximal stenosis level, and only trefoil and triangle stenosis grade types were considered because of sufficient sample size. Intraobserver and interobserver reliability were assessed by calculating weighted kappa coefficients. Symptom severity was evaluated via the Japanese Orthopedic Association Back Pain Evaluation Questionnaire (JOABPEQ). Walking ability was assessed using the Self-Paced Walking Test (SPWT) and JOABPEQ subscales. Demographic characteristics, SPWT scores, and JOABPEQ scores were compared between patients with trefoil and triangle stenosis types. RESULTS: The mean patient age was 58.1 (standard deviation, 8.4) years. The kappa values of the MRI morphology stenosis grade types showed a perfect agreement between the stenosis grade types. The trefoil group (n=53) and triangle group (n=45) showed similar preoperative JOABPEQ subscale scores (e.g., low back pain, lumbar function, and mental health) and were not significantly different in age, BMI, duration of symptoms, or lumbar stenosis levels (all p>0.05); however, trefoil stenosis grade type was associated with a decreased walking ability according to the SPWT and JOABPEQ subscale scores. CONCLUSIONS: These findings suggest preoperative walking ability is more profoundly affected in patients with trefoil type stenosis than in those with triangle type stenosis.