Development of a system to assess the two- and three-dimensional bone mineral density of the lumbar vertebrae from clinical quantitative CT images

Quantitative CT lumbar spine BMD cutpoint value for classifying osteoporosis among older East Asian women should be lower than the value for Caucasians

For Caucasian women, the QCT (quantitative CT) lumbar spine (LS) bone mineral density (BMD) cutpoint value for classifying osteoporosis is 80 mg/ml. At the age of approximate 78 years, US Caucasian women QCT LS BMD population mean is 80 mg/ml, while that of Chinese women and Japanese women is around 50 mg/ml. Correlation analyses show, for Chinese women and Japanese women, QCT LS BMD of 45 mg/ml corresponds to the dual-energy X-ray absorptiometry cutpoint value for classifying osteoporosis. For Chinese and Japanese women, if QCT LS BMD 80 mg/ml is used as the threshold to classify osteoporosis, then the specificity of classifying subjects with vertebral fragility fracture into the osteoporotic group is low, whereas threshold of 45 mg/ml approximately achieve a similar separation for women with and without vertebral fragility fracture as the reports for Caucasian women. Moreover, by using 80mg/ml as the cutpoint value, LS QCT leads to excessively high prevalence of osteoporosis for Chinese women, with the discordance between hip dual-energy X-ray absorptiometry and LS QCT measures far exceeding expectation. Considering the different bone properties and the much lower prevalence of fragility fractures in the East Asian women compared with Caucasians, we argue that the QCT cutpoint value for classifying osteoporosis among older East Asian women will be close to and no more than 50 mg/ml LS BMD. We suggest that it is also imperative the QCT osteoporosis classification criterion for East Asian male LS, and male and female hips be re-examined.

The effect of forearm rotation on the bone mineral density measurements of the distal radius

INTRODUCTION

Forearm dual-energy X-ray absorptiometry (DXA) is often performed in clinics where central DXA is unavailable. Accurate bone mineral density (BMD) measurement is crucial for clinical assessment. Forearm rotation can affect BMD measurements, but this effect remains uncertain. Thus, we aimed to conduct a simulation study using CT images to clarify the effect of forearm rotation on BMD measurements.

MATERIALS AND METHODS

Forearm CT images of 60 women were analyzed. BMD was measured at the total, ultra-distal (UD), mid-distal (MD), and distal 33% radius regions with the radius located at the neutral position using digitally reconstructed radiographs generated from CT images. Then, the rotation was altered from - 30° to 30° (supination set as positive) with a one-degree increment, and the percent BMD changes from the neutral position were quantified for all regions at each angle for each patient.

RESULTS

The maximum mean BMD changes were 5.8%, 7.0%, 6.2%, and 7.2% for the total, UD, MD, and distal 33% radius regions, respectively. The analysis of the absolute values of the percent BMD changes from the neutral position showed that BMD changes of all patients remained within 2% when the rotation was between - 5° and 7° for the total region, between - 3° and 2° for the UD region, between - 4° and 3° for the MD region, and between - 3° and 1° for the distal 33% radius region.

CONCLUSION

Subtle rotational changes affected the BMD measurement of each region. The results showed the importance of forearm positioning when measuring the distal radius BMD.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.