Background: Osteoporosis, characterized by reduced bone mass and increased fracture risk, presents a significant public health challenge. Dual-energy X-ray Absorptiometry (DEXA) scans offer a reliable means of assessing bone mineral density (BMD) and detecting osteoporosis. The aim of this study is to assess awareness, attitude, and practice of women in the Jazan region toward DEXA scan. Methods: Data were collected through a self-administered online questionnaire. Statistical analysis was conducted using SPSS version 29.0. The total knowledge scores of participants were calculated to assess potential associations with sociodemographic data. A p-value of less than 0.05 was considered statistically significant. Results: Among 400 women, 230 (57.5%) had low knowledge scores primarily due to poor knowledge of the DEXA scan procedure. Merely 39 women had undergone a DEXA scan mainly due to medical prescription (n = 22, 56.4%). Total knowledge scores were significantly higher among women with high monthly incomes (p = 0.019) and those working in medical-related jobs (p = 0.017). Conclusions: This study underscores the need for targeted interventions to improve awareness of DEXA scans among women in Jazan City. Additionally, the findings suggest that socioeconomic factors may influence awareness levels, emphasizing the importance of tailored educational strategies to reach diverse demographics.

Background: Neck and/or shoulder pain (NSP) represents a significant public health burden worldwide. However, nationally representative data from Saudi Arabia remain limited as most existing studies have focused on specific occupational groups or localized populations. This study examines the prevalence, determinants, and risk factors of NSP among Saudi adults. Materials and Methods: A cross‐sectional study was conducted between May and September 2024 among 2171 adults across Saudi Arabia. Data were collected from participants aged ≥ 18 years using a structured, validated questionnaire assessing NSP prevalence, sociodemographic characteristics, lifestyle factors, and comorbidities. Result: Among 2171 participants (mean age of 29 ± 12), 54% were females. The overall point prevalence of neck pain was 39.1%, while shoulder pain affected 38.6% of participants. Higher odds of neck pain were associated with use of electronic devices (OR = 1.82, p = 0.003), occupational tasks with extended standing (OR = 1.53, p = 0.003), and history of trauma (OR = 2.52, p < 0.001). Shoulder pain was associated with prolonged use of electronic devices (OR = 2.14, p < 0.001) and history of trauma (OR = 4.77, p < 0.001). Notably, regular exercise and male sex were protective against both neck pain and shoulder pain compared to females. Conclusion: NSP is common among Saudi adults and linked to modifiable lifestyle and work‐related factors, underscoring the need for prevention, ergonomic measures, and regular pain management. Long‐term studies are needed to evaluate its impact and treatment outcomes.

Background/Objectives: Incidental findings (IFs) on emergency computed tomography (CT) are increasingly common as cross-sectional imaging utilization expands. Data from the Middle East are limited. We aimed to determine the report-documented frequency, anatomical distribution, and proportion of clinically actionable IFs on emergency abdominopelvic CT examinations at a tertiary-care center in southwestern Saudi Arabia. Methods: This retrospective cross-sectional study reviewed 705 consecutive emergency abdominopelvic CT examinations performed at King Fahd Central Hospital, Jazan, between 1 January 2023 and 31 December 2025. Two senior radiology residents (R3 and R4) extracted findings unrelated to the presenting indication from consultant-approved reports, applying a predefined terminology-standardization framework. Clinical actionability was defined a priori as a documented recommendation for further evaluation or follow-up. Descriptive statistics with 95% Wilson confidence intervals (CI) were calculated using R Software. Results: At least one IF was identified in 138 of 705 examinations, yielding a patient-level report-documented rate of 19.6% (95% CI 16.8% to 22.7%). A total of 255 IFs were recorded, averaging 1.85 findings per affected patient. The mean age was 42.3 ± 18.2 years; 78 patients (56.5%) were male and 116 (84.1%) were Saudi nationals. Findings were most often within the abdominopelvic cavity (159; 62.4%), followed by miscellaneous (64; 25.1%) and thoracic-vascular (32; 12.5%). At the secondary-category level, the most prevalent groups were spine (38; 14.9%), hepatobiliary (37; 14.5%), and genitourinary (31; 12.2%). Documented further work-up was recommended in 48 of 138 patients with IFs (34.8%; 95% CI 27.3% to 43.0%). Conclusions: Approximately one in five emergency abdominopelvic CT examinations contained incidental findings, with one-third requiring further evaluation. Spine, hepatobiliary, and genitourinary findings were most common. Variability in reported rates likely reflects differences in study methods and definitions. Future multicenter studies should evaluate standardized reporting approaches and follow-up strategies, together with their impact on reporting consistency, downstream investigations, and patient outcomes.

Background: Breast cancer remains a major public health burden, and improvements in imaging-based detection, risk stratification, and diagnostic workflow may support earlier diagnosis, reduce false-negative interpretations, and improve population-level screening efficiency. Recent advances in quantitative breast imaging, radiomics, and artificial intelligence (AI) have expanded the role of imaging beyond lesion detection toward tumor characterization, prognostication, and precision medicine. However, evidence regarding their diagnostic and predictive performance remains heterogeneous. This study aimed to systematically evaluate the diagnostic accuracy of quantitative imaging biomarkers, radiomics, and AI-based approaches in breast cancer and to assess their associations with tumor biological characteristics. Methods: A systematic review and meta-analysis were conducted in accordance with the PRISMA 2020 guidelines. MEDLINE (PubMed), Embase, Scopus, Web of Science, Cochrane CENTRAL, and IEEE Xplore were searched for studies published between January 2015 and December 2024. Eligible studies evaluated quantitative imaging biomarkers, radiomics, or AI applications in breast cancer diagnosis or characterization. Diagnostic performance measures, including sensitivity, specificity, area under the curve (AUC), and biomarker associations with pathological features, were extracted. A random-effects meta-analysis was performed to pool AUC values where appropriate. Results: Thirteen studies involving quantitative imaging biomarkers (n = 5), radiomics (n = 4), and AI-based detection systems (n = 4) met the inclusion criteria. For differentiation of benign and malignant breast lesions, apparent diffusion coefficient (ADC) measurements demonstrated excellent diagnostic performance, with a pooled AUC of 0.94 (95% CI: 0.91-0.97). Individual studies reported sensitivities ranging from 84.1% to 92.5% and specificities from 90.2% to 91.1%, with optimal ADC thresholds between 1.23 and 1.30 × 10−3 mm2/s. Across studies evaluating tumor grade, lower ADC values were generally associated with higher-grade tumors; however, quantitative pooling was not retained because of the small number of contributing studies, non-independent comparisons, and substantial heterogeneity. Radiomics studies achieved molecular subtype classification accuracies ranging from 77% to 100%, particularly when derived from magnetic resonance imaging (MRI) and ADC maps. AI systems showed excellent diagnostic performance, with AUCs ranging from 0.876 to 0.97, consistently matching or exceeding radiologist performance. Hybrid AI-radiologist approaches yielded the highest diagnostic accuracy and reduced false-negative interpretations. Conclusion: Quantitative imaging biomarkers, particularly ADC, provide useful diagnostic information and may offer prognostic insights in breast cancer. Radiomics shows strong potential for noninvasive molecular characterization, while AI-based systems achieve high diagnostic accuracy and improve clinical workflow when integrated with radiologist interpretation. These technologies represent promising components of precision breast imaging.