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Opportunistic osteoporosis screening using multidetector CT-scans (MDCT) and convolutional neural network (CNN)-derived segmentations of the spine to generate volumetric bone mineral density (vBMD) bears the potential to improve incidental osteoporotic vertebral fracture (VF) prediction. However, the performance compared to the established manual opportunistic vBMD measures remains unclear. Hence, we investigated patients with a routine MDCT of the spine who had developed a new osteoporotic incidental VF and frequency matched to patients without incidental VFs as assessed on follow-up MDCT images after 1.5 years. Automated vBMD was generated using CNN-generated segmentation masks and asynchronous calibration. Additionally, manual vBMD was sampled by two radiologists. Automated vBMD measurements in patients with incidental VFs at 1.5-years follow-up (n = 53) were significantly lower compared to patients without incidental VFs (n = 104) (83.6 ± 29.4 mg/cm3 vs. 102.1 ± 27.7 mg/cm3, p < 0.001). This comparison was not significant for manually assessed vBMD (99.2 ± 37.6 mg/cm3 vs. 107.9 ± 33.9 mg/cm3, p = 0.30). When adjusting for age and sex, both automated and manual vBMD measurements were significantly associated with incidental VFs at 1.5-year follow-up, however, the associations were stronger for automated measurements (β = −0.32; 95% confidence interval (CI): −20.10, 4.35; p < 0.001) compared to manual measurements (β = −0.15; 95% CI: −11.16, 5.16; p < 0.03). In conclusion, automated opportunistic measurements are feasible and can be useful for bone mineral density assessment in clinical routine.

SummaryOur study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures.IntroductionMany patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures.MethodsIn total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation.ResultsThe random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64).ConclusionThe presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.

PurposeThis scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans.MethodsPubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings.ResultsFourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection.ConclusionsThe review highlights AI’s promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.

Summary Osteoporosis remains under-diagnosed. Routine abdominal CT can provide opportunistic screening, but the effect of IV contrast is largely unknown. The overall performance for predicting osteoporosis was similar between enhanced and unenhanced scans. Therefore, both non-contrast and contrast-enhanced abdominal CT scans can be employed for opportunistic osteoporosis screening. Introduction Osteoporosis is an important yet under-diagnosed public health concern. Lumbar attenuation measurement at routine abdominal CT can provide a simple opportunistic initial screen, but the effect of IV contrast has not been fully evaluated. Methods Mean trabecular CT attenuation values (in Hounsfield units, HU) at the L1 vertebral level were measured by oval region-of-interest (ROI) on both the unenhanced and IV-contrast-enhanced CT series in 157 adults (mean age, 62.0). All patients underwent correlative central DXA within 6 months of CT. Based on DXA BMD of the lumbar spine, femoral neck, and total proximal femur: osteoporosis, osteopenia, and normal BMD was present in 33, 77, and 47, respectively. Statistical analysis included Bland-Altman plots and receiver operating characteristic (ROC) curves. Results Mean difference (±SD) in L1 trabecular attenuation between enhanced and unenhanced CT series was +11.2 HU (±19.2) (95 % CI, 8.16–14.22 HU), an 8 % difference. Intra-patient variation was substantial, but no overall trend in the HU difference was seen according to underlying BMD. ROC area under the curve (AUC) for unenhanced and enhanced CT for diagnosing osteoporosis were similar at 0.818 and 0.830, respectively ( p  = 0.632). Thresholds for maintaining 90 % specificity for osteoporosis were 90 HU for unenhanced and 102 HU for enhanced CT. Thresholds for maintaining 90 % sensitivity for osteoporosis were 139 HU for unenhanced and 144 HU for enhanced CT. Similar diagnostic performance was seen for diagnosing low BMD (osteoporosis or osteopenia) using higher HU cut-offs. Conclusion Contrast-enhanced CT shows an average increase of 11 HU over the unenhanced series for L1 trabecular attenuation. The overall performance for predicting osteoporosis is similar between the enhanced and unenhanced scans, thus either can be employed for initial opportunistic screening.

Summary Vertebral bone quality (VBQ) score is an opportunistic measure of bone mineral density using routine preoperative MRI in spine surgery. VBQ score positively correlates with age and is reproducible across serial scans. However, extrinsic factors, including MRI machine and protocol, affect the VBQ score and must be standardized. Purpose The purposes of this study were to determine whether VBQ score increased with age and whether VBQ remained consistent across serial MRI studies obtained within 3 months. Methods This retrospective study evaluated 136 patients, age 20–69, who received two T1-weighted lumbar MRI within 3 months of each other between January 2011 and December 2021. VBQ(L1-4) score was calculated as the quotient of L1–L4 signal intensity (SI) and L3 cerebral spinal fluid (CSF) SI. VBQ(L1) score was calculated as the quotient of L1 SI and L1 CSF SI. Regression analysis was performed to determine correlation of VBQ(L1-4) score with age. Coefficient of variation (CV) was used to determine reproducibility between VBQ(L1-4) scores from serial MRI scans. Results One hundred thirty-six patients (mean ± SD age 44.9 ± 12.5 years; 53.7% female) were included in this study. Extrinsic factors affecting the VBQ score included patient age, MRI relaxation time, and specific MRI machine. When controlling for MRI relaxation/echo time, the VBQ(L1-4) score was positively correlated with age and had excellent reproducibility in serial MRI with CV of 0.169. There was excellent agreement (ICC > 0.9) of VBQ scores derived from the two formulas, VBQ(L1) and VBQ(L1-4). Conclusion Extrinsic factors, including MRI technical factors and age, can impact the VBQ(L1-4) score and must be considered when using this tool to estimate bone mineral density (BMD). VBQ(L1-4) score was positively correlated with age. Reproducibility of the VBQ(L1-4) score across serial MRI is excellent especially when controlling for technical factors, supporting use of the VBQ score in estimating BMD. The VBQ(L1) score was a reliable alternative to the VBQ(L1-4) score.

PurposeTo evaluate the use of the modified and simplified vertebral bone quality (VBQ) method based on T1-weighted MRI images of S1 vertebrae in assessing bone mineral density (BMD) for patients with lumbar degenerative diseases.MethodsWe reviewed the preoperative data of patients with lumbar degenerative diseases undergoing lumbar spine surgery between January 2019 and June 2022 with available non-contrast T1-weighted magnetic resonance imaging (MRI), computed tomography (CT) images and dual-energy X-ray absorptiometry (DEXA). S1 vertebral bone quality scores (S1 VBQ) and S1 CT Hounsfield units were measured with picture archiving and communication system (PACS). One-way ANOVA was applied to present the discrepancy between the S1 VBQ of patients with normal bone density (T-score ≥ − 1.0), osteopenia (− 2.5 < T-score < − 1.0) and osteoporosis (T-score ≤ − 2.5). The receiver operating characteristic curve (ROC) was drawn to analyze the diagnostic performance of S1 VBQ in distinguishing low BMD. Statistical significance was set at p < 0.05.ResultsA total of 207 patients were included. The S1 VBQ were significantly different between groups (p < 0.001). Interclass correlation coefficient for inter-rater reliability was 0.86 (95% CI 0.78–0.94) and 0.94(95% CI 0.89–0.98) for intra-rater reliability. According to the linear regression analysis, the S1 VBQ has moderate-to-strong correlations with DEXA T-score (r = − 0.48, p < 0.001). The area under the ROC curve indicated a predictive accuracy of 82%. A sensitivity of 77.25% with a specificity of 70% could be achieved for distinguishing low BMD by setting the S1 VBQ cutoff as 2.93.ConclusionsThe S1 VBQ was a promising tool in distinguishing poor bone quality in patients with lumbar degenerative diseases, especially in cases where the previously reported VBQ method based on L1–L4 was not available. S1 VBQ score could be useful as opportunistic assessment for screening and complementary evaluation to DEXA T-score before surgery.

PurposeThis study investigated the vertebral bone quality (VBQ) score as a potential tool for opportunistic osteoporosis screening and its correlation with dual-energy X-ray absorptiometry (DXA) values.MethodsIn a single-center retrospective cohort of 130 patients, VBQ and DXA measures were compared using various statistical analyses. The optimal VBQ threshold for predicting osteoporosis was determined using receiver operating characteristic (ROC) analysis.ResultsVBQ exhibited a significant negative association with DXA values, suggesting that higher VBQ scores are indicative of lower bone density. Age and VBQ were significant predictors of osteoporosis, with both increasing the log-odds of the condition. An optimal VBQ threshold of 2.7 was determined, demonstrating fair discriminatory power and high negative predictive value.ConclusionThe study highlighted the potential of VBQ as a diagnostic tool for osteoporosis with high intra- and inter-observer reliability. The optimal VBQ threshold of 2.7 can aid in ruling out osteoporosis and identifying individuals for further evaluation.

PurposeFully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures.Materials and methodsInitial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison.ResultsMean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, − 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post  + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction.ConclusionFully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.

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.

PurposeTo assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis, and sarcopenia using artificial intelligence (AI) body composition algorithms.MethodsMarkov models were constructed and 10-year simulations were performed on hypothetical age- and sex-specific cohorts of 10,000 U.S. adults (base case: 55 year olds) undergoing abdominal CT. Using expected disease prevalence, transition probabilities between health states, associated healthcare costs, and treatment effectiveness related to relevant conditions (CV disease/osteoporosis/sarcopenia) were modified by three mutually exclusive screening models: (1) usual care (“treat none”; no intervention regardless of opportunistic CT findings), (2) universal statin therapy (“treat all” for CV prevention; again, no consideration of CT findings), and (3) AI-assisted abdominal CT-based opportunistic screening for CV disease, osteoporosis, and sarcopenia using automated quantitative algorithms for abdominal aortic calcification, bone mineral density, and skeletal muscle, respectively. Model validity was assessed against published clinical cohorts.ResultsFor the base-case scenarios of 55-year-old men and women modeled over 10 years, AI-assisted CT-based opportunistic screening was a cost-saving and more effective clinical strategy, unlike the “treat none” and “treat all” strategies that ignored incidental CT body composition data. Over a wide range of input assumptions beyond the base case, the CT-based opportunistic strategy was dominant over the other two scenarios, as it was both more clinically efficacious and more cost-effective. Cost savings and clinical improvement for opportunistic CT remained for AI tool costs up to $227/patient in men ($65 in women) from the $10/patient base-case scenario.ConclusionAI-assisted CT-based opportunistic screening appears to be a highly cost-effective and clinically efficacious strategy across a broad array of input assumptions, and was cost saving in most scenarios.