Opportunistic AI-enabled automated bone mineral density measurements in lung cancer screening and coronary calcium scoring CT scans are equivalent

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review

PURPOSE

This 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.

METHODS

PubMed, 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.

RESULTS

Fourteen 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.

CONCLUSIONS

The 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.

Opportunistic use of chest low-dose computed tomography (LDCT) imaging for low bone mineral density and osteoporosis screening: cutoff thresholds for the attenuation values of the lower thoracic and upper lumbar vertebrae

Background

Osteoporosis remains substantially underdiagnosed and undertreated worldwide. Chest low-dose computed tomography (LDCT) may provide a valuable and popular opportunity for osteoporosis screening. This study sought to evaluate the feasibility of the screening of low bone mineral density (BMD) and osteoporosis with mean attenuation values of the lower thoracic compared to upper lumbar vertebrae. The cutoff thresholds of the mean attenuation values in Hounsfield units (HU) were derived to facilitate implementation of opportunistic screening using chest LDCT.

Methods

The participants aged 30 years or older who underwent chest LDCT and quantitative computed tomography (QCT) examinations from August 2018 to October 2020 in our hospital were consecutively included in this retrospective study. A region of interest (ROI) was placed in the trabecular bone of each vertebral body to measure the HU values. The correlations of mean HU values of lower thoracic (T11-T12) and upper lumbar (L1-L2) vertebrae with age and lumbar BMD obtained with QCT were performed using the Pearson correlation coefficient, respectively. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was generated to determine the cutoff thresholds for distinguishing low BMD from normal and osteoporosis from non-osteoporosis.

Results

A total of 1,112 participants were included in the final study cohort (743 men and 369 women, mean age 58.2±8.9 years; range, 32-88 years). The mean HU values of T11-T12 and L1-L2 were significantly different among 3 QCT-defined BMD categories of osteoporosis, osteopenia, and normal (P<0.001). The differences in HU values between T11-T12 and L1-L2 in each category of bone status were statistically significant (P<0.001). The mean HU values of T11-T12 (r=-0.453, P<0.001) and L1-L2 (r=-0.498, P<0.001) had negative correlations with age. Positive correlations were observed between the mean HU values of T11-T12 (r=0.872, P<0.001) and L1-L2 (r=0.899, P<0.001) with BMD. The optimal cutoff thresholds for distinguishing low BMD from normal were average T11-T12 ≤157 HU [AUC =0.941, 95% confidence interval (CI): 0.925-0.954, P<0.001] and L1-L2 ≤138 HU (AUC =0.950, 95% CI: 0.935-0.962, P<0.001), as well as distinguishing osteoporosis from non-osteoporosis were average T11-T12 ≤125 HU (AUC =0.960, 95% CI: 0.947-0.971, P<0.001) and L1-L2 ≤107 HU (AUC =0.961, 95% CI: 0.948-0.972, P<0.001). There was no significant difference between the AUC values of T11-T12 and L1-L2 for low BMD (P=0.07) and osteoporosis (P=0.92) screening.

Conclusions

We have conducted a study on low BMD and osteoporosis screening using mean attenuation values of lower thoracic and upper lumbar vertebrae. Assessment of mean attenuation values of T11-T12 and L1-L2 can be used interchangeably for low BMD and osteoporosis screening using chest LDCT, and their cutoff thresholds were established.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images

Background

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs).

Methods

In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated.

Results

In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively.

Conclusions

The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

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.