ABSTRACT
Introduction: Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists. Research question: This review investigates the utilization of ML and DL in TLIs causing VFs. Materials and methods: Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis. Results: Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137). Conclusion: The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.
ABSTRACT
Aim To investigate the correlation of body mass index (BMI) with severity of intervertebral disc degeneration. Methods The study enrolled patients who had undergone surgical intervention for a herniated disc at the Department of Neurosurgery of the Cantonal Hospital Zenica. Patients underwent thorough preoperative evaluation, including medical history, neurological and physical assessments, and radiological analysis. The surgical intervention consisted of a posterior lumbar discectomy, and the excised disc material was preserved and subjected to histopathological analysis based on Histopathologic Degeneration Score (HDS). Patients were divided in two groups according to Body Mass Index (BMI): study group with BMI≥25 and control group with BMI<25. Results Among 69 patients with herniated IVD, 26 (37.7%) were with BMI≥25 (study group), and 43 (62.3%) were with BMI<25 (controls). The study group displayed substantial increase in height, 1.80±0.06 m compared to controls, 1.74±0.06 m (p=0.001). Weight and BMI were significantly higher in the study group of patients (weight: 91.60±10.22 vs. 67.37±9.20 kg, BMI: 28±2 vs. 22±2; p<0.001). Differences were confirmed in HDS values in the study group comparing to the control group (p<0.001). The study group exhibited significant differences in chondrocyte proliferation, tears and clefts, granular changes, and mucous degeneration (p<0.05), and positive correlations were found between BMI and these alterations found in the herniated discs (p<0.05). Therefore, HDS showed positive correlations with BMI (R=0.599; p<0.001) and weight (R=0.696; p<0.001). Conclusion The study's findings confirmed that BMI has a significant impact on intervertebral disc degeneration, emphasizing the importance of weight management in preventing disc degeneration.
