This study aims to develop a robust artificial intelligence (AI) model for predicting
Bone Mineral Density (BMD) from X-ray images using deep learning techniques, with a
particular focus on improving the model's generalizability across diverse populations.
The purpose is to provide an accessible, non-invasive screening tool for osteoporosis,
reducing dependency on dual-energy X-ray absorptiometry (DEXA) scans, which are often
unavailable or unaffordable in low-resource settings such as Bangladesh. Leveraging the
convolutional neural network (CNN) architecture, this AI model is expected to assist in
early osteoporosis diagnosis and management, ultimately improving clinical
decision-making and healthcare efficiency.
This case-control observational study will be conducted in the Radiology Department of
Ibn Sina Diagnostic and Consultation Center, Uttara. The study comprises both prospective
and retrospective data collection phases, allowing for comprehensive data aggregation.
During the prospective phase, data will be collected directly from eligible patients
undergoing X-ray imaging and DEXA scans. For the retrospective phase, historical data
will be extracted from clinical databases, including X-ray images and corresponding BMD
reports. The study aims to address variations in bone health across a broad demographic,
reflecting the prevalence of osteoporosis among different ages, genders, and clinical
backgrounds in Bangladesh.
In Bangladesh, osteoporosis remains underdiagnosed due to the limited availability of
DEXA scanners and trained personnel, particularly in rural and resource-constrained
areas. The standard diagnostic pathway often begins with symptomatic X-ray imaging,
followed by a DEXA scan if osteoporosis is suspected. This two-step process is costly and
time-consuming, delaying diagnosis and treatment, which can lead to serious
complications, including fractures. AI-driven predictions of BMD from X-ray images have
the potential to streamline this pathway, enabling cost-effective screening and
prioritization of patients who may need further DEXA-based testing. The AI model will be
trained using a comprehensive dataset that includes demographic and clinical
covariates-such as age, gender, menopausal status, and comorbid conditions like diabetes
and cardiovascular disease-capturing correlations that could enhance prediction accuracy.
Ultimately, the goal is to offer a reliable, scalable solution for osteoporosis screening
that could be integrated into existing clinical workflows and alleviate the need for DEXA
in settings where it is unavailable.
The study targets a diverse population group, including individuals with normal bone
density, osteopenia, and osteoporosis as defined by DEXA measurements. This diversity
ensures that the AI model can account for a wide spectrum of patient profiles and enhance
its predictive robustness. The population will consist of adults across all age groups
and genders, including both symptomatic and asymptomatic individuals.
The study will follow a structured protocol for data collection, aiming to gather
comprehensive information on patients that may influence bone health. Key variables will
include demographic details such as age, gender, and menopausal status; clinical
variables like the presence of comorbidities such as diabetes and cardiovascular disease,
BMI, and history of fractures; and imaging and diagnostic results, specifically X-ray
images (spine or hip) and DEXA scan results for ground truth BMD values. In the
prospective phase, eligible patients undergoing X-ray or DEXA scans will be approached
for consent, and upon agreement, their clinical and demographic data will be recorded,
including a unique identifier to ensure data integrity and confidentiality. Anonymized
X-ray and DEXA images will then be collected, forming the primary dataset for AI
training. The retrospective phase will involve data extraction from existing clinical
records, focusing on spine and hip X-ray images and corresponding BMD results.
Identifiable patient information will be removed to protect privacy. This historical
dataset will complement the prospective data, providing a broader spectrum of cases and
contributing to model generalizability.
The AI model will be developed using CNN architecture tailored for image-based
prediction. Exploratory data analysis will be conducted initially to understand the
distribution of key demographics and clinical factors, which will inform the balance and
structure of the dataset. Once the data is cleaned and processed, the CNN model will be
trained to predict BMD values directly from X-ray images, with actual DEXA measurements
serving as ground truth. Model performance will be evaluated using metrics critical for
clinical application, including Mean Absolute Error (MAE) and Pearson Correlation
Coefficient (PCC) to assess prediction accuracy, as well as Area Under the
Precision-Recall Curve (AUPRC) and overall accuracy to measure diagnostic robustness. To
further ensure accuracy, a k-fold cross-validation technique will be applied, generating
mean values and standard deviations for each metric, thereby providing insight into the
model's consistency. Comparisons between various CNN architectures and training
methodologies will identify the optimal approach for BMD prediction.
Upon completion, the AI model will serve as an assistive diagnostic tool for BMD
assessment from X-ray images, with several anticipated applications. First, the model
will support early detection of osteoporosis by identifying low BMD values, enabling
clinicians to detect osteoporosis earlier in the diagnostic pathway and potentially
improving patient outcomes. Second, it will aid clinical decision-making by allowing
healthcare professionals to prioritize patients for further testing, particularly in
resource-limited settings. Third, the analysis of clinical covariates such as age,
gender, and comorbidities with BMD could refine risk assessment, supporting more
personalized osteoporosis management strategies. Lastly, the structured storage of
images, BMD values, and clinical data will support future bone health research, enhancing
osteoporosis screening and preventive care capabilities.
This study requires no additional facilities beyond those already available within the
clinical radiology departments for X-ray and DEXA scanning. Existing data storage
capabilities will support data management, ensuring compliance with privacy and security
standards for patient information. In summary, this study's AI model seeks to deliver a
viable, scalable solution for osteoporosis screening by offering accurate, non-invasive
BMD predictions from X-ray images. This approach has the potential to improve healthcare
access, especially in rural and low-resource settings, where it can function as a
screening tool that mitigates the dependence on costly DEXA scans.