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Purpose of Review Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening. Recent Findings A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Summary Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.
Purpose of Review Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening. Recent Findings A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Summary Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.
BACKGROUND: Cone beam computed tomography is widely applied for diagnostics and planning various manipulations in the maxillofacial region, for example, dental implantation. Its advantages include high spatial resolution, low radiation exposure, and cost-effectiveness. However, it has a significant drawback: the inability to determine the density of the jaw bone in Hounsfield Units (HU). AIMS: This study aimed to develop radiopaque templates with sets of X-ray density based on potassium hydrophosphate and beta-tricalcium phosphate, to study templates on various cone beam computed tomography and multidetector computed tomography devices, and to determine a cross-calibration algorithm for assessing the bone mineral density of the jaw in HU. MATERIALS AND METHODS: The bone mineral density template comprised microtubes (0.25 ml) with potassium hydrophosphate concentrations of 49.96, 99.98, 174.99, 349.99, and 549.98 mg/ml, and a suspension of beta-tricalcium phosphate with an equivalent concentration of potassium hydrophosphate 1,506 mg/ml, designed to simulate the types of bone density according to C. Mish. The study was carried out on two multidetector computed tomography and four cone beam computed tomography machines. Cross-calibration was referred on the standard multidetector computed tomography 1 mode 120 kV, 200 mA. RESULTS: There was a significant scatter of the X-ray values (HU for multidetector computed tomography and GV for cone beam computed tomography) vs. bone mineral density, with varying slopes, bias, and curve shapes. After cross-calibration, good comparability corresponding to the multidetector computed tomography 1 mode was shown. The median of the differences before cross-calibration was 160 relative units (HU, GV), after decreased by 10 times and amounted to 16 rel. units (p=0.000). The mean difference for cone beam computed tomography was significantly higher (30 rel. units) than for multidetector computed tomography (8 rel. units) (p=0.024, MannWhitney U test). CONCLUSION: The developed radiopaque template enables the standardization of densitometric indicators for cone beam computed tomography and various multidetector computed tomography modes. On average, the spread after cross-calibration is reduced by 10 times, which makes it possible to classify bone tissue in HU according to C. Mish.
Goal: To develop a method for automated assessment of the volumetric bone mineral density (BMD) of the vertebral bodies using an artificial intelligence (AI) algorithm and a phantom modeling method.Materials and Methods: Evaluation of the effectiveness of the AI algorithm designed to assess BMD of the vertebral bodies based on chest CT data. The test data set contains 100 patients aged over 50 y.o.; the ratio between the subjects with/without compression fractures (Сfr) is 48/52. The X-ray density (XRD) of vertebral bodies at T11-L3 was measured by experts and the AI algorithm for 83 patients (205 vertebrae). We used a recently developed QCT PK (Quantitative Computed Tomography Phantom Kalium) method to convert XRD into BMD followed by building calibration lines for seven 64-slice CT scanners. Images were taken from 1853 patients and then processed by the AI algorithm after the calibration. The male to female ratio was 718/1135.Results: The experts and the AI algorithm reached a strong agreement when comparing the measurements of the XRD. The coefficient of determination was R2=0.945 for individual vertebrae (T11-L3) and 0.943 for patients (p=0.000). Once the subjects from the test sample had been separated into groups with/without Сfr, the XRD data yielded similar ROC AUC values for both the experts – 0.880, and the AI algorithm – 0.875. When calibrating CT scanners using a phantom containing BMD samples made of potassium hydrogen phosphate, the following averaged dependence formula BMD =0.77*HU-1.343 was obtained. Taking into account the American College Radiology criteria for osteoporosis, the cut-off value of BMD<80 mg/ml was 105.6HU; for osteopenia BMD<120 mg/ml was 157.6HU. During the opportunistic assessment of BMD in patients aged above 50 years using the AI algorithm, osteoporosis was detected in 31.72% of female and 18.66% of male subjects.Conclusions: This paper demonstrates good comparability for the measurements of the vertebral bodies’ XRD performed by the AI morphometric algorithm and the experts. We presented a method and demonstrated great effectiveness of opportunistic assessment of vertebral bodies’ BMD based on computed tomography data using the AI algorithm and the phantom modeling.
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