Background: Osteoporosis is commonly evaluated using dual-energy X-ray absorptiometry (DXA) for bone mineral density (BMD). Non-contrast computed tomography (CT) scans provide an alternative for opportunistic osteoporosis assessment. This study aimed to evaluate screening thresholds for osteoporosis based on CT attenuation values in Hounsfield units (HU) of L1–L4 vertebrae from CT scans of the abdominal region, compared to DXA assessments of the lumbar spine and hips. Methods: Conducted retrospectively over approximately two years, the analysis included 109 patients who had both CT and DXA scans within 12 months, excluding those with metal artifacts affecting the vertebrae. CT attenuation values in the trabecular region of the vertebrae were measured and compared among three groups based on the lowest T-score from DXA. Results: In a predominantly female cohort (mean age 66.3 years), the lowest CT attenuation values for L1–L4 vertebrae showed a moderate correlation with the lowest T-score, with a Pearson correlation coefficient of 0.542 (95% CI: 0.388, 0.667). A HU threshold of ≤142 at the L1 vertebra showed 91.9% sensitivity and 48.4% specificity, while a threshold of ≤160 HU showed 97.3% sensitivity and 31.3% specificity for screening osteoporosis. Conclusions: This study supports the use of non-contrast CT with these HU thresholds as an opportunistic tool for osteoporosis assessment.

Osteoporosis is commonly evaluated using dual-energy X-ray absorptiometry (DXA) for bone mineral density (BMD). Non-contrast computed tomography (CT) scans provide an alternative for opportunistic osteoporosis assessment. This study aimed to evaluate screening thresholds for osteoporosis based on CT attenuation values in Hounsfield units (HU) of L1-L4 vertebrae from abdominal CT scans, compared to DXA assessments of the lumbar spine and hips. Conducted retrospectively over about two years, the analysis included 109 patients who had both CT and DXA scans within 12 months, excluding those with metal artifacts affecting the vertebrae. CT attenuation values in the trabecular region of the vertebrae were measured and compared among three groups based on the lowest T-score from DXA. In a predominantly female cohort (mean age 66.3 years), significant correlations were found between the CT HU values of L1-L4 vertebrae and the lowest T-score. CT HU values differed significantly among normal (17.4%), low bone mass (46.8%), and osteoporosis (35.8%) groups (P < 0.001). A HU threshold of ≤142 at the L1 vertebra showed 91.9% sensitivity and 48.4% specificity, while a threshold of ≤160 HU showed 97.3% sensitivity and 31.3% specificity for screening osteoporosis. In conclusion, this study supports the utility of non-contrast CT with these HU thresholds as an opportunistic osteoporosis assessment.

Bone Mineral Density and Trabecular Bone Score in Predicting Vertebral Fractures in Male Employees of the Electricity Generating Authority of Thailand: Purpose. Osteoporotic VF is frequently asymptomatic and affects not only women but also men. Identifying patients at risk is essential for early management and prevention. BMD and the TBS are measurements of bone strength and trabecular microarchitecture, respectively. Their role in VF prediction in men is less well-studied. We determined the BMD and TBS predictive ability for osteoporotic VF in men. Methods. A total of 115 male participants of the Electricity Generating Authority of Thailand (EGAT) cohorts without a history of VF who completed the baseline BMD and TBS measurements in 2012 and a thoracolumbar spine radiograph in 2017 were recruited. The VF was assessed using the Genant semiquantitative method. Logistic regression analysis was performed to identify factors associated with the fracture. The area under the receiving operator curve (AUC) was analyzed to define VF predictive ability. Results. Forty subjects (34.78) had VFs. The unadjusted relative risks (95 confidence interval) for VF for one standard deviation decrease in the TBS and low TBS were 1.319 (1.1571.506) and 2.347 (1.4963.682), respectively, and remained significant after BMD and age adjustment. For VF prediction, combined models had a greater AUC than models predicted from a single variable. The use of low TBS, femoral neck BMD, and age provided the best AUC (0.693). Conclusion. BMD and the TBS could predict osteoporotic VF in male EGAT employees. The use of both BMD and the TBS in the VF prediction process improved predictive ability.

Background: Nationwide data on the incidence of central precocious puberty (CPP) and premature thelarche (PT), a benign variant of pubertal development, are limited.Purpose: To evaluate trends in CPP and PT incidence among Thai children before, during, and after the COVID-19 pandemic.Methods: This cross-sectional population-based study used National Health Security Office (NHSO) claims data from 2018 to 2024. CPP and PT were identified using ICD-10-TM (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Thai Modification) codes. Annual incidence rates (AIRs) were calculated for each NHSO-insured population, while incidence rate ratios (IRRs) were estimated using quasi-Poisson regression.Results: A total of 6,782 girls and 189 boys with CPP, and 1,594 girls with PT, were included. Among girls, the CPP AIR increased from 15.0 per 100,000 person-years in 2018 to 51.7 in 2021 and remained elevated during 2022–2024 (44.1–47.7). The IRR was 2.27 (95% confidence interval [CI], 1.82–2.83) for pandemic versus prepandemic, and 1.06 (95% CI, 0.89–1.26) for postpandemic versus pandemic period. An age-stratified analysis showed the greatest increase among girls aged 8 to <9 years. Among boys, the CPP AIR remained low and stable (0.6–1.0 per 100,000 person-years). The female-to-male IRR was 43.0 (95% CI, 33.5–55.3). The incidence of PT in girls increased from 7.2 in 2018 to 14.6 per 100,000 person-years in 2024, with the largest increase observed among those aged 7 to <8 years.Conclusion: The incidence of CPP among Thai girls increased during the COVID-19 pandemic and remained elevated thereafter, whereas the incidence of PT increased gradually. Both trends were most pronounced in peripubertal age groups. Further studies are required to determine whether these patterns reflect pandemic-related factors or an earlier onset of puberty.

Background Machine learning is nowadays commonly used for disease prediction, including cardiovascular disease. There is growing evidence of the effectiveness of machine learning algorithms for stroke risk prediction models. Aims A systematic review was conducted to identify and comprehensively evaluate the available evidence. Summary of review Relevant studies were identified from the three electronic databases (i) MEDLINE via Pubmed, (ii) Scopus, and (iii) IEEE Xplore from inception to 1^st December 2020. Out of 12,626 studies identified, 40 used machine learning for ischemic or hemorrhagic stroke risk prediction models. Synthesis without meta-analysis identified that a boosting algorithm (median C-statistics = 0.9 (interquartile range [IQR]: 0.88-0.92)), and neural network (median C-statistic = 0.80 (IQR: 0.77-0.92)) performed best among ML models in the low risk of bias studies. Moreover, a boosting algorithm also performed best in overall (both low and high risk of bias) studies (median C-statistic = 0.92 (IQR: 0.90-0.95)). Conclusions The systematic review found promising results of the ML algorithm model performances compare with the gold standard conventional models, such as FSRP (C-statistic 0.653) and revised FSRP (C-statistic 0.716). In term of the algorithm, boosting and neural networks are robust, but are considered as black-box models, since they are composed of non-linearity and complex algorithms. It remains questionable whether a physician would adapt these algorithms to use in a real clinical setting. Moreover, less than half of the studies (16 out of 40) were at low risk of bias in our systematic review. More researches with good methodology and study design, alongside explainable and good performance models, may become available in the future. Trial Registration Information The International Prospective Register of Systematic Reviews (PROSPERO) database (ID: CRD42021234081).