Abdulmonem Alsiddiky

Background: Scoliosis is a common musculoskeletal disorder that remains underrecognized in Saudi Arabia, where limited public awareness may delay early detection and treatment. This study assessed public knowledge, support for school-based screening, and attitudes toward treatment. Methods: A nationwide cross-sectional survey was conducted during the 2025 Saudi National Scoliosis Awareness Campaign through public venues and online platforms. A culturally adapted questionnaire assessed demographics, knowledge, symptom recognition, treatment preferences, barriers, and information sources. A total of 2055 responses were analyzed using descriptive statistics and chi-square tests. Results: While 70.9% of participants had heard of scoliosis, only 18.1% reported good understanding. Symptom recognition was limited, with spinal curvature, back pain, and uneven shoulders most often identified. Most respondents supported school-based screening (88.2%) and early detection (92.1%), with a mean preferred screening age of 8 years. Bracing (64.3%) and surgery (53.5%) were more accepted than observation (30.5%), though concerns about pain, effectiveness, and appearance were frequent. Social media was the most common initial information source (34.6%), while healthcare professionals were the preferred future source (79.2%). Reported barriers included lack of awareness (68.2%), difficulty accessing specialists (19.9%), and cost (8.9%). Conclusions: Public support for school-based screening is strong, but baseline knowledge remains limited, and treatment perceptions are shaped by psychosocial and cultural concerns. National campaigns, school curricula, and healthcare-led education, combined with accessible, low-cost screening, may enhance early detection and improve scoliosis management in Saudi Arabia.

Recently, vertebral tumor prediction using image-based vertebral bio-mathematical modeling with accurate segmentation has been considered as a significant area of research. Further, precise lumbar spinal segmentation is essential to the clinicians for tumor analysis. Therefore, precise and reliable segmentation process is required to help radiologists and doctors to identify different vertebrae tumors with better prediction ratio. The exact vertebral disks segmentation of spinal bones from medical images is a complex process in dealing with different deformities and pathologies in accordance to the conventional techniques such as Deep Convolutional Neural Network (DCNN), Finite Element Analysis (FEA), Principal Component and Factor Analysis (PCFA), Multi-Parameter Ensemble Learning (MPEL), Hierarchical Conditional Random Forest (HCRF), and Deep Siamese Neural Network (DSNN). Therefore, to overcome the present drawbacks, Analytical Transform Assisted Statistical Characteristic Decomposition Model (ATS-CDM) is proposed in this paper for the accurate prediction of vertebral tumor detection and segmentation. This technique is used for the calibration of the segmentation procedures in vertebral tumor image prediction and Receiver Operating Curve (ROC) grading for the lumbar spines. The significant objective of the model-fitting algorithm iterates the tumor regions and measures the current region's variance for the accurate identification of tumor. The outcomes show potential and promising results at lab scale evaluation through analyzing the vertebral datasets with Intra-Discal Pressure (IDP) images for experimental validation with 98.7% bending and 98.88 % segmentation accuracy leads to 94.2 ± 0.2 % to 97.02 ± 0.2 % average ROC grading.