Development and Evaluation of an Artificial Intelligence Model for Bone Mineral Density Prediction From X-Ray Images
Conditions: Osteoporosis
Sponsor: Bangladesh University of Engineering and Technology

 
 
 
 
 
 
 
Official Title 
Development and Evaluation of an Artificial 
Intelligence Model for Bone Mineral Density Prediction 
from X-Ray Images 
 
 
Date 
06th June, 2024 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
Page | 1 
Application for Ethical Clearance 
Ibn Sina Medical College 
1/1-B, Kallyanpur, Dhaka-1216, Bangladesh 
 
1. 
Principal Investigator(s):  
Name: Dr. Taufiq Hasan 
Qualification: BSc, MSc, PhD (UT Dallas, TX, USA) 
Detail Address: Department of Biomedical Engineering, Bangladesh 
University of Engineering and Technology (BUET), Dhaka – 1205. 
Mobile: 01817579844              Telephone (Off./Res): N/A 
E-mail: taufiq@bme.buet.ac.bd  
2. 
Co-Principal Investigator(s):  
Name: Dr. A.S.M. Shahidul Hossain 
Qualification: MBBS (DMC), MD (DU) 
Detail Address: Ibn Sina Diagnostic and Consultation Center, Uttara 
House#52, Garib-E-Newaz Avenue, Sector#13, Uttara, Dhaka-1230 
Mobile: 01743001629             Telephone (Off./Res): N/A 
E-mail: asmshahidul74@gmail.com 
3. 
Place of the Study/Institution(s):  
i. 
Ibn Sina Diagnostic and Consultation Center, Uttara 
House#52, Garib-E-Newaz Avenue, Sector#13, Uttara, 
Dhaka-1230 
Other Ibn Sina branches if needed. 
4. 
Title of Study: Development and Evaluation of an Artificial 
Intelligence Model for Bone Mineral Density Prediction from X-Ray 
Images 
5. 
Type of Study: Prospective and retrospective study. 
6. 
Duration of Study: 6 months 
7. 
Total Cost: N/A 
8. 
Funding Agency: RISE-BUET 
 
 

 
 
Page | 2 
I agree to obtain approval of the Ibn Sina Ethical Review Committee for 
any changes involving the rights and welfare of subjects or any changes 
of the methodology before making any such changes. 
 
_______________________________________________________________ 
Dr. Taufiq Hasan  
Associate Professor, Biomedical Engineering,  
Bangladesh University of Engineering and Technology (BUET), Dhaka – 1205.  
Date: 
 
 
_______________________________________________________________ 
Dr. A.S.M. Shahidul Hossain  
Radiology & Imaging Specialist, 
Ibn Sina Diagnostic and Consultation Center, Uttara, 
House#52, Garib-E-Newaz Avenue, Sector#13, Uttara, Dhaka-1230. 
Date:  
 
 

 
 
 
Page | 3 
 
 
Circle the appropriate answer to each of the following 
(If not Applicable write NA) 
 
1. Source of Population: 
 
(a)  ILL Participant         
Yes  
No 
   
(b)  Non ILL Participant  Yes  
No 
   
(c)  Minors or persons  
Yes  
No  
       
under guardianship 
 
2. Does the study involve? 
 
   
(a)  Physical risks  
Yes  
No  
 
 
To the subjects  
 
   
(b)  Social Risks         
Yes  
No 
 
   
(c)  Psychological  
Yes  
No  
 
 
Risks to subjects 
 
   
(d)  Discomfort to  
Yes  
No  
       
Subjects  
   
(e)  Invasion of the body  Yes  
No   
   
(f)  Invasion of Privacy  
Yes  
No  
   
(g)  Disclosure of  
Yes  
No  
 
 
Information damaging to  
       
Subject or others 
 
3. Does the study involve? 
 
 
(a)  Use of records,  
Yes  
No  
       
(Hospital, medical,  
       
Death, birth or other) 
 
  
(b)  Use of fetal tissue  
Yes  
No  
       
Or abortus 
  
(c)  Use of organs or  
Yes  
No  
       
Body fluids 
 
 
 
 
 
4. Are subjects clearly informed about? 
  
(a)  Nature and         
Yes  
No  
 
       purposes of study 
  
(b)  Procedures to be  
N/A  
 
       
followed including  
 
 
alternatives used 
  
 
 
(c)  Physical risks  
N/A 
 
  
(d)  Private questions 
Yes  
No 
      (e)  Invasion of the  
N/A  
 
       
Body 
 
  
(f)  Benefits to be  
Yes  
No 
     
Derived 
 
  
(g)  Right to refuse  
Yes  
No 
       
to participate or  
       
to withdraw from study 
 
  
(h)  Confidential  
Yes  
No 
       
handling of data 
 
  
(i)  Compensation  
N/A  
 
      
where there are risks or  
 
 
loss of working time or  
 
 
privacy is involved in  
 
 
any particular procedure 
5. Will signed consent form/verbal consent 
be required? 
  
(a)  From Subjects  
Yes  
No 
 
  
(b)  From parent or  
Yes  
No  
       
guardian (if subjects  
 
 
are minors) 
6. Will precautions be  
Yes  
No  
 
taken to protect  
   anonymity of subjects

 
 
 
Page | 4 
 
 
 
RESEARCH PROTOCOL 
 
● Project Title: Development and Evaluation of an Artificial Intelligence 
Model for Bone Mineral Density Prediction from X-Ray Images  
 
Summary: Osteoporosis, a pervasive skeletal disorder characterized by 
diminished bone strength predisposing individuals to an increased risk of 
fractures, presents a substantial public health challenge globally [1][2]. It's 
estimated that osteoporosis and its consequent increase in fracture risk 
significantly contribute to morbidity, mortality, and economic costs [3][4]. 
Despite the availability of effective treatments, the condition often remains 
undiagnosed and untreated until a fracture occurs, underscoring the critical 
need for early detection and intervention [5][6]. 
Dual-energy X-ray absorptiometry (DEXA) is the gold standard for assessing 
bone mineral density (BMD) and fracture risk [7]. However, its utility is 
hampered by limited availability, especially in rural and low-resource 
settings, such as Bangladesh, where osteoporosis prevalence is notably high 
[8]. The scarcity of DEXA units exacerbates the challenge of osteoporosis 
screening and management, leaving a significant portion of the population 
at risk [9]. 
In this context, plain X-ray imaging, widely available even in resource-
constrained settings, emerges as a promising alternative for osteoporosis 
screening. Recent advancements in deep learning and computer vision offer 
the potential to automate the analysis of X-ray images for BMD estimation. 
The objective of this thesis is to develop and evaluate an Artificial 
Intelligence (AI)-based model that predicts BMD from plain X-ray images of 
the lumbar spine and pelvis. By leveraging Convolutional Neural Networks 
(CNNs), this model aims to identify individuals at risk of osteoporosis and 
osteopenic conditions without the need for DEXA. The proposed AI model 
processes X-ray images to detect subtle changes in bone texture and density, 
potentially offering a rapid, non-invasive, and cost-effective tool for large-
scale osteoporosis screening, particularly beneficial in regions like 
Bangladesh where DEXA is scarcely available. This research addresses the 
critical gap in osteoporosis screening and diagnosis, aiming to contribute 
significantly to public health by enabling earlier detection and management 
of osteoporosis, thereby reducing the incidence of fractures and associated 
healthcare costs [10]. 
 
● Introduction: Globally, osteoporosis poses a significant public health 
challenge, with an estimated 200 million patients affected worldwide and 
about it is responsible for nearly 9 million fractures annually, indicating its 
profound impact on morbidity, mortality, and healthcare costs [11]. In 
Bangladesh, the prevalence of osteoporosis is concerning, with a study 
revealing that among a cohort of 526 individuals, 37.3% are at risk of 
developing the condition [8]. The risk is notably higher in females (43.2%) 
compared to males (30.3%) and escalates with age, particularly after 50 
years, reflecting global trends. Furthermore, the presence of diabetes and 
cardiovascular diseases significantly increases the risk, with a striking 78% 
of cardiovascular patients being at risk. Notably, individuals with 
multimorbidity present an even higher prevalence rate of 90%. This 
underlines an urgent need for effective screening and early detection 
strategies to mitigate the burden of osteoporosis, especially in resource-
constrained settings. 

 
 
 
Page | 5 
 
 
 
Recent advancements in deep learning, particularly deep learning, have 
shown promise in utilizing plain radiographs for BMD prediction, potentially 
bypassing the need for more expensive and less accessible DEXA scans 
[12][13]. Studies leveraging CNNs have been successful in extracting 
clinically relevant features from X-ray images to predict BMD and assess 
fracture risk, demonstrating AI's potential in osteoporosis screening [14]. 
Despite these advancements, the application of AI in osteoporosis diagnosis 
faces challenges, notably the generalizability of models across diverse 
populations and healthcare settings. Most existing studies utilize datasets 
from localized patient populations and are often limited by the availability 
and quality of annotated data. This raises concerns about the models' 
performance and applicability in different regions, particularly in low- and 
middle-income countries where the disease burden is highest, and healthcare 
resources are scarce. 
To address these gaps, our research aims to develop and evaluate an AI 
model for BMD prediction from X-ray images, with a particular focus on 
enhancing model generalizability and diagnostic accuracy. We intend to 
collect 
a 
comprehensive 
dataset 
comprising 
X-ray 
images, 
BMD 
measurements from local healthcare facilities. This diverse dataset will 
enable us to train and validate AI models that can robustly predict 
osteoporosis across different patient populations. 
Our goal is to create an AI-assisted diagnostic tool that can be easily 
integrated into clinical workflows, offering a cost-effective and accessible 
solution for osteoporosis screening. 
 
 
● Objectives: The primary objective is to curate a comprehensive dataset of 
X-ray images sourced from publicly available domains and local health 
facilities, encompassing a diverse representation of patient demographics, 
geographic locations, and equipment variations. Building upon this dataset, 
we seek to develop a robust artificial intelligence model capable of prediction 
of bone mineral density from X-ray images. 
 
The specific objectives of the study are: 
 
a) To develop a deep learning convolutional neural network (CNN) model 
that can accurately predict Bone Mineral Density (BMD) values from X-
ray images, using both prospective and retrospective data collection 
methods. 
b) To validate the AI model's performance on a diverse dataset 
encompassing a broad demographic, including variables such as age, 
gender, height, weight, menopausal status, and the presence of 
comorbidities like cardiovascular disease and diabetes. 
c) To evaluate the model's performance using a range of metrics, including 
Area Under the Precision-Recall Curve (AUPRC), accuracy, Mean 
Absolute Error (MAE), and Pearson Correlation Coefficient (PCC), 
ensuring its robustness in predicting BMD. 
d) To assess the usability and interpretability of the AI model's output by 
healthcare professionals, determining its integration into clinical practice 
and its impact on the diagnostic workflow efficiency. 
e) To measure the generalizability and adaptability of the model across 
various healthcare settings, ensuring the AI system's reliability on X-ray 
images from different populations and regions. 

 
 
 
Page | 6 
 
 
 
f) To investigate the model's potential in enhancing early detection and 
treatment planning for osteoporosis by providing a non-invasive, cost-
effective tool for BMD estimation, potentially leading to better patient 
management and outcomes. 
g) To assess the AI model's role as a decision support tool in determining 
the necessity for additional diagnostic procedures, such as DEXA scans, 
thereby optimizing patient care pathways and potentially reducing 
healthcare costs. 
 
● Rationale: The early detection and treatment of osteoporosis are crucial in 
preventing fractures and ensuring the quality of life for individuals at risk. 
However, in developing countries like Bangladesh, the standard method for 
assessing bone health, DEXA, is not widely accessible due to its cost and the 
scarcity of specialized equipment and trained personnel. Consequently, 
many cases of osteoporosis remain undiagnosed until a fracture occurs, 
which significantly impacts patient outcomes and healthcare costs. 
The current pathway for osteoporosis suspicion in Bangladesh typically 
begins with an X-ray prompted by symptoms of pain or fracture. If 
osteoporosis is suspected, a subsequent BMD test is recommended. This two-
step process not only increases the financial burden on patients but also 
delays the diagnosis and treatment of osteoporosis, especially in rural areas 
where BMD testing facilities are scarce. 
Addressing these challenges, our research proposes the use of deep learning 
algorithms to predict BMD values directly from X-ray images, which are 
more readily available and cost-effective than DEXA scans. By training a 
CNN with a dataset that includes demographic and clinical covariates, such 
as age, gender, menopausal status, cardiovascular disease, and diabetes, our 
AI model aims to identify correlations that can accurately predict BMD [15]. 
In this way, the developed AI model is intended to function as an assistive 
tool, providing a preliminary assessment to guide physicians on whether a 
more expensive and resource-intensive DEXA scan is warranted. This 
approach could substantially streamline the diagnostic process, making it 
more efficient and potentially reducing unnecessary medical expenses for 
patients. 
 
● Methodology: In our data-driven approach to developing a predictive model 
for Bone Mineral Density (BMD) from X-ray images, we will collect and 
utilize a comprehensive dataset that includes X-ray images and BMD reports 
alongside demographic and clinical details of the subjects. The methodology 
will ensure patient privacy and data security, with all personally identifiable 
information being excluded from the dataset. 
 
a) Study Population: The study is structured as a prospective and 
retrospective investigation aimed at developing and evaluating an AI-
based diagnostic tool for the prediction of BMD from X-ray image 
analysis. The population will include subjects with varying bone 
densities as determined by DEXA scans, which serve as the gold 
standard for BMD measurement. The study will target a diverse 
demographic to account for the variability in bone health across 
different ages, genders, and clinical backgrounds. 
 
 
 

 
 
 
Page | 7 
 
 
 
▪ 
Inclusion criteria: 
● Subjects of any gender and age group (18 and above) for 
whom X-ray imaging of relevant skeletal sites (e.g., 
spine, hip) is available. 
● Individuals willing to participate and who have 
provided informed consent for the use of their X-ray 
images and clinical data for research purposes. 
● Subjects with both X-ray images and DEXA scan 
results. 
● Accessibility to supplementary medical records that 
may contribute to the model's predictive accuracy, such 
as historical data on fractures, calcium levels, and 
other osteoporosis-related factors. 
 
▪ 
Exclusion criteria: 
● Subjects for whom X-ray images or clinical data are 
incomplete or of insufficient quality for analysis. 
● Individuals with medical conditions that could 
significantly alter bone density independently of 
osteoporosis, such as bone cancers or certain metabolic 
diseases. 
● Subjects who have undergone treatments or procedures 
that 
might 
significantly 
impact 
bone 
density 
measurement, such as long-term steroid use or recent 
orthopedic surgeries. 
● Pregnant women, given the potential impact on 
screening 
results 
and 
the 
need 
for 
special 
considerations during pregnancy. 
 
 
b) Sample size calculation: Following the methodology of sample size 
calculation to evaluate diagnostic performance (for algorithms in this 
case), we use the following method of calculation for a target 
sensitivity and specificity incorporating disease prevalence [16]:  
 
𝑁𝑠𝑒𝑛≥ 
𝑍2(1−𝛼)/2 × 𝑠𝑒𝑛 (1 −𝑠𝑒𝑛)
𝑐2 × 𝑝
 
 
𝑁𝑠𝑝𝑒𝑐≥ 
𝑍2(1−𝛼)/2 × 𝑠𝑝𝑒𝑐 (1 −𝑠𝑝𝑒𝑐)
𝑐2 × (1 −𝑝)
 
 
Where, 
 
𝑁𝑠𝑒𝑛  
= 
Required sample size for sensitivity, 𝑠𝑒𝑛   
𝑁𝑠𝑝𝑒𝑐  
= 
Required sample size for specificity, 𝑠𝑝𝑒𝑐 
𝛼  
= 
Confidence level 
𝑍2(1−𝛼)/2  =  
The Z-score for the confidence level 𝛼 
𝑝  
=  
Prevalence of osteoporosis 
𝑐  
=  
Precision 
𝑠𝑒𝑛 
= 
Estimated sensitivity 
𝑠𝑝𝑒𝑐 
= 
Estimated specificity 

 
 
 
Page | 8 
 
 
 
 
The final sample size 𝑁 is calculated based on the larger of the two 
values 𝑁𝑠𝑒𝑛 and 𝑁𝑠𝑝𝑒𝑐. In this study, as we are using deep learning 
techniques, we need the medical image samples to reflect the various 
osteoporosis, osteopenia and normal cases. For our analysis, we 
assume an 95% confidence level, precision of 0.05, and prevalence of 
osteoporosis, 𝑝 = 0.373 [8]. We estimate the sensitivity and specificity 
of our algorithms to be at 91.1% and 68.9% respectively (𝑠𝑒𝑛= 0.911 
and 𝑠𝑝𝑒𝑐= 0.689) [17]. Using these values, we obtain the sample size 
values: 𝑁𝑠𝑒𝑛=   335  and 𝑁𝑠𝑝𝑒𝑐=  526. Since 𝑁𝑠𝑝𝑒𝑐> 𝑁𝑠𝑒𝑛, the final 
sample size for the study will be: 
 
𝑁= 𝑁𝑠𝑒𝑛= 526 
We can set a target of N = 600. 
 
c) Procedures: In the prospective phase of the study, data collection 
will be conducted within clinical settings. Eligible patients will be 
approached during X-ray scans and BMD tests and presented with a 
consent form for their consideration. Upon agreement, a unique 
identifier will be assigned to maintain data integrity while preserving 
patient anonymity. Following the consent process, patients will 
provide essential clinical information. Healthcare professionals will 
perform an X-ray or DEXA scan, and the results will be generated and 
collected. This approach ensures the acquisition of non-identifiable 
information, unique data identifiers, and informed consent directly 
within the clinical setting, aligning with the specific requirements of 
the study. 
In the retrospective phase of the study, additional data will be 
extracted from the clinic's database. We will collect all spine and hip 
X-ray images and BMD reports from the radiology department. The 
identification and matching process will rely on patient ID, phone 
number, date of birth, and other shared clinical information present 
in both the X-ray and BMD reports. Given the retrospective nature of 
this approach, tracking down and obtaining consent from individual 
patients is not feasible. To address privacy concerns, any personally 
identifiable patient information will be systematically removed from 
the collected data. This strategy ensures compliance with privacy 
standards while utilizing valuable information from previous 
screenings. Although informed consent cannot be obtained for this 
specific dataset, the study will strictly adhere to ethical guidelines 
and prioritize patient privacy throughout the data extraction process 
from the clinic's database. 
 
d) Methods of Data Collection: The following fields will be recorded 
in the data collection for this study: 
▪ 
Patient ID (unique identification number) 
▪ 
Date & time of the data input 
▪ 
Age 
▪ 
Gender 
▪ 
Phone number 
▪ 
X-ray image (hip or spine) 

 
 
 
Page | 9 
 
 
 
▪ 
DEXA scan result 
▪ 
BMI 
▪ 
Menopausal status 
▪ 
Diabetes 
▪ 
Cardiovascular disease and other clinical covariates 
 
The X-ray images, and DEXA scan result of the subjects along with 
the relevant clinical covariates will be used to design the algorithms. 
The standard test status and the corresponding result will be used as 
the ground truth for our detection algorithm. 
 
e) Data Interpretation: Upon the conclusion of data collection, 
exploratory data analysis to assess the distribution of patients across 
different demographics and clinical factors, including age, gender, 
menopausal status, presence of cardiovascular diseases, diabetes, and 
their correlation with BMD values. This process will ensure a 
comprehensive understanding of the dataset's characteristics and the 
establishment of a balanced dataset that represents a wide spectrum 
of patient profiles. 
The refined dataset will serve as the foundation for training and 
evaluating our AI model, specifically designed for the regression-
based prediction of BMD from X-ray images. Our AI algorithm will 
leverage CNN to process X-ray images and predict BMD values, 
aiming to identify potential cases of osteopenia, osteoporosis, and 
normal. 
To ascertain the model's performance and its clinical applicability, we 
will compare the AI-generated predictions with actual BMD 
measurements obtained from DEXA scans. This validation process 
will involve collaboration with orthopedic specialists and radiologists 
who will review the AI predictions in the context of their clinical 
experience and available patient information. 
 
f) Statistical Analysis: Upon finalizing the dataset, our statistical 
analysis will concentrate on evaluating the artificial intelligence 
model's capability to predict BMD from X-ray images accurately. 
Performance metrics such as Mean Absolute Error (MAE), Pearson 
Correlation Coefficient (PCC), Area Under the Precision-Recall Curve 
(AUPRC), and accuracy will be employed to meticulously assess the 
model's performance. To ensure the reliability of our findings, we will 
implement k-fold cross-validation techniques, providing a framework 
for evaluating our model across various subsets of data. This approach 
will allow us to calculate mean values and standard deviations for 
each performance metric, offering a comprehensive understanding of 
the model's predictive accuracy and consistency. The culmination of 
our analysis will involve a comparative review of different CNN 
architectures and training methodologies to determine the most 
effective strategy for BMD prediction from X-ray images. 
 
Utilization of Results: The study results will play a crucial role in the 
development and evaluation of an automatic BMD prediction algorithm for 
osteoporosis screening. The algorithm's primary objectives are: 
 
 
 

 
 
 
Page | 10 
 
 
 
a) BMD Prediction: To accurately predict BMD values from X-ray 
images, facilitating an automated assessment tool for early detection 
of osteoporosis and osteopenia. This tool aims to bridge the gap in 
accessibility to BMD testing, especially in resource-limited settings. 
 
b) Clinical Decision Support: To aid healthcare professionals by 
providing automated BMD assessments, enhancing their ability to 
make informed decisions regarding patient care. The model's 
predictions can help prioritize patients who need further testing or 
intervention, streamlining the diagnostic process. 
 
c) Correlation Analysis: To explore and identify significant 
relationships between BMD or osteoporosis and collected clinical 
covariates such as age, gender, menopausal status, cardiovascular 
diseases, and diabetes. This analysis aims to enhance understanding 
of the factors influencing bone health, contributing to personalized 
and more precise osteoporosis risk assessment and management 
strategies. 
 
d) Data Management for Bone Health Research: To enable systematic 
storage and organization of X-ray images along with their 
corresponding BMD values and clinical data. This will not only 
support future research in the field of bone health but also ensure the 
privacy and security of patient data throughout the process. 
 
These outcomes are poised to significantly enhance the efficiency and 
accuracy of osteoporosis screening, providing valuable support to healthcare 
professionals in Bangladesh. 
 
● 
Facilities: For the collection of data for our thesis on predicting Bone 
Mineral Density (BMD) from X-ray images, no extra facilities are necessary 
beyond those already available in the radiology departments (e.g., X-ray 
imaging, DEXA scan, and data storage capabilities). 
 
● Approval / Forwarding of the Head of Department / Institute / IRB: 
A forwarding letter from the Department of Biomedical Engineering, 
Bangladesh University of Engineering and Technology (BUET) is attached. 
 
● Flow Chart: The data collection activity and the corresponding timeline is 
described in the Gantt chart below: 
 
No. 
Work/Activities 
Months 
1 
2 
3 
4 
1 
Initial algorithm with publicly available 
data 
 
 
 
 
2 
Collection of images, reports and metadata 
 
 
 
 
3 
Homogenization and organization of data 
 
 
 
 
3 
Data processing and analysis 
 
 
 
 

 
 
 
Page | 11 
 
 
 
 
 
● Ethical Implications: The proposed retrospective/prospective study based 
on medical records is designed to develop and evaluate the diagnostic 
performance of AI algorithms that will predict BMD from X-ray images. The 
study poses minimal to no risk to the subjects according to the HHS (USA) 
definition. 
 
● References:  
1. O. Johnell and J. A. Kanis, “An estimate of the worldwide prevalence and 
disability associated with osteoporotic fractures,” Osteoporos. Int., vol. 17, no. 12, 
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2. L. Sànchez-Riera et al., “The global burden attributable to low bone mineral 
density,” Ann. Rheum. Dis., vol. 73, no. 9, pp. 1635–1645, Sep. 2014, doi: 
10.1136/annrheumdis-2013-204320. 
3. A. S. Nazrun, M. N. Tzar, S. A. Mokhtar, and I. N. Mohamed, “A systematic 
review of the outcomes of osteoporotic fracture patients after hospital discharge: 
morbidity, subsequent fractures, and mortality,” Ther. Clin. Risk Manag., vol. 10, 
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11. P. Pisani et al., “Major osteoporotic fragility fractures: Risk factor updates and 
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Page | 13 
 
 
 
PART - C 
INFORMED CONSENT FORM (PROSPECTIVE STUDY) 
 
Title:  
 
Development and Evaluation of an Artificial Intelligence 
Model for Bone Mineral Density Prediction from X-Ray Images 
 
Coordinator: 
Dr. Taufiq Hasan, Associate Professor,  
 
 
Department of Biomedical Engineering,  
Bangladesh University of Engineering and Technology, 
Dhaka – 1205  
PURPOSE OF PROJECT 
You are invited to participate in a project entitled Development and Evaluation 
of an Artificial Intelligence Model for Bone Mineral Density Prediction from 
X-Ray Images. This project focuses on developing an algorithm that will 
automatically predict BMD value from X-ray images.  
You are selected as a possible participant in the study. 
*If you decide to terminate your participation in this study, you should notify Dr. 
Taufiq Hasan at +8801817579844. 
PROCEDURES 
If you choose to participate, you will provide essential clinical information during 
BMD test. The recorded data includes: 
 Age 
 Gender 
 Phone number 
 BMD test results 
 BMI 
 Menopausal status 
 Diabetes 
 Cardiovascular disease 
Furthermore, your X-ray images will be collected. Some personally identifiable data 
will be used for correlating X-ray images with BMD reports. However, no personally 
identifiable data is stored in the final dataset, ensuring privacy. The collected 
dataset will train the AI algorithm for automatic prediction, emphasizing patient 
privacy throughout the process. 

 
 
 
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THE PROCEDURE IS NON-INVASIVE 
The data collected during these sessions will be put into one of the databases of the 
Department of BME, BUET, and will be used for future research.  
No traditionally used identifying information about you, such as your name, 
address, telephone number, or national ID number, will be put into the public 
database. Your privacy is very important to us, and we will use safety measures to 
protect it. Despite all the safety measures that we will use, we cannot guarantee 
that your identity will never become known. 
PARTICIPANT RESPONSIBILITIES 
As a participant, your responsibilities include: 
 
● Follow the instructions of the data collection coordinators and study staff.  
● Tell the data collection coordinator or research study staff if you feel any 
discomforts. 
● Ask questions as you think of them. 
● Inform the coordinator or research staff if you change your mind about 
staying in the study.  
WITHDRAWAL FROM STUDY 
If you first agree to participate and then you change your mind, you are free to 
withdraw your consent and discontinue your participation at any time.   
If you decide to withdraw your consent to participate in this study, you can directly 
inform Dr. Taufiq Hasan at +8801817579844 or notify the study staff. 
If you withdraw from the study for any reason all the data acquired from you will 
be removed from the databases. 
YOU MUST NOTIFY YOUR DISSENT WITHIN THE PERIOD OF THE 
STUDY BEING CONDUCTED. 
The Protocol Director may also withdraw you from the study without your consent 
for one or more of the following reasons:  
● Failure to follow the instructions during data collection. 
● The Protocol Director decides that continuing your participation could be 
harmful to you. 
● You need other treatments that are not allowed in the study. 
● The study is canceled. 
● Unanticipated circumstances. 
● Other administrative reasons. 
 
 

 
 
 
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POSSIBLE RISKS, DISCOMFORTS, AND INCONVENIENCES  
There is no risk associated with the study other than the risk due to radiation which 
is present in any radiographic test. You should contact the Protocol Director or the 
study staff if you have any questions. 
POTENTIAL BENEFITS 
We cannot and do not guarantee or promise that you will receive any benefits from 
this study. The study is being conducted for humanitarian purposes, which envision 
a long-term goal that will benefit patients suffering from or at the risk of having 
osteoporosis. 
PARTICIPANT’S RIGHTS 
You should not feel obligated to agree to participate. Your questions should be 
answered clearly and to your satisfaction.  If you decide not to participate, notify the 
project coordinator or the study staff. 
CONFIDENTIALITY 
The results of this research study may be presented at scientific or medical meetings 
or published in scientific journals. Your identity and/or your personal health 
information will not be disclosed except as authorized by you or as required by law.   
CONTACT INFORMATION  
Questions, Concerns, or Complaints:  If you have any questions, concerns or 
complaints about this research study, its procedures, risks and benefits, or 
alternative courses of treatment, you should ask the data collection coordinator, Dr. 
Taufiq Hasan.  You should also contact him at any time if you feel you have been 
harmed by being a part of this study at +8801817579844. 
 
 

 
 
 
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May we contact you about future studies that may be of interest to you?   
(Yes/No) ______ 
 
Are you participating in any other research studies? 
(Yes/No) _____ 
 
Signing your name means you agree to be in this study. 
 
_____________________________________ 
                       
_________________ 
Signature of Adult Participant 
 
 
 
     
 
Date 
 
_____________________________________     
Name of Adult Participant                                         
 

 
 
 
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Questionnaire Form 
Project Title: Development and Evaluation of an Artificial Intelligence Model for 
Bone Mineral Density Prediction from X-Ray Images 
 
General Information 
Have you had your X-ray done 
at Ibn Sina, Uttara within the 
last 3 months? 
  □ (1) Yes     □ (2) No 
If yes, what type of X-ray did you do? 
  □ (1) Lumbar spine   □ (2) Hip    □ (3) Others 
X-ray ID 
Date 
Participant Information 
Patient ID 
Date of birth 
Patient’s name 
 
Occupation 
Contact no. 
Gender 
  □ (1) Male 
  □ (2) Female 
Is written informed consent obtained? 
  □ (1) Yes     □ (2) No 
Date of interview 
 
Name of interviewer 
 
Anthropometric information 
Height (Feet/Inches) 
Weight (Kg/Kilogram) 
Have you had any fractures? 
  □ (1) Yes     □ (2) No 
If yes, specify the affected area 
Clinical Information 
Menopause age 
   
No. of Pregnancies 
Diabetes 
  □ (1) Yes     □ (2) No 
Cardiovascular disease 
  □ (1) Yes     □ (2) No 
Parent fractured hip 
  □ (1) Yes     □ (2) No 
Currently smoking 
  □ (1) Yes     □ (2) No 
Glucocorticoids 
  □ (1) Yes     □ (2) No 
Rheumatoid arthritis 
  □ (1) Yes     □ (2) No 
Secondary osteoporosis 
  □ (1) Yes     □ (2) No 
Alcohol 3 or more units/day 
  □ (1) Yes     □ (2) No 

 
 
 
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JUSTIFICATIONS FOR INFORMED CONSENT WAIVER 
(RETROSPECTIVE STUDY) 
 
The proposed research is a study that only uses medical records of X-ray 
examinations and DEXA scans. As the number of samples required is large (600 
according to our sample size calculation) compared to the monthly recorded cases, it 
is not possible to return to the patients for acquiring their consent in case of 
previously recorded data. Considering the high potential impact of the study, we 
earnestly request waiver of informed consent. The justification for this request is 
provided below: 
1. On the topic of informed consent waiver, the regulatory authority of the United 
States, Department of Health and Human Services (HHS) mentions in 45 C.F.R. 
§ 46.116(d): 
 
“An IRB may approve a consent procedure which does not include, or which 
alters, some or all of the elements of informed consent set forth in this 
section, or waive the requirements to obtain informed consent provided the 
IRB finds and documents that:  
(1) The research involves no more than minimal risk to the subjects;  
(2) The waiver or alteration will not adversely affect the rights and welfare 
of the subjects;  
(3) The research could not practicably be carried out without the waiver or 
alteration; …” 
2. On the topic of case control studies, the Bangladesh Medical Research Council 
(BMRC) “Ethical Guidelines for Conducting Research Studies Involving Human 
Subjects” mentions on Page 45: 
  
“However, if it entails only a review of medical records, informed consent may 
not be required and indeed may very often not be feasible”. 
 
In light of the abovementioned regulatory guidelines described by HHS (USA) and 
BMRC, we want to highlight that our study fulfills all of the above conditions as 
explained below: 
1. The proposed research involves no more than minimal risk to subjects. According 
to HHS document 45 C.F.R. § 46.102(i): 
 
“Minimal risk means that the probability and magnitude of harm or 
discomfort anticipated in the research are not greater in and of themselves 
than those ordinarily encountered in daily life or during the performance of 
routine physical or psychological examinations or tests.”  
We will use de-identified medical records for the study. This falls into the 
category of minimal risk as described above. 

 
 
 
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2. The research could not be carried out practicably without the waiver or 
alteration. Training artificial intelligence algorithms requires a huge amount of 
data from a large number of patients due to the overfitting problem (further 
detailed in the sample size calculation section below). It is practically not 
possible to collect signed or verbal informed consent from each patient from each 
hospital. 
3. The waiver or alteration will not adversely affect the rights and welfare of the 
subjects. Only their digital medical images and clinical history data will be used 
for the research. Personal identifying information (name, etc.) will be removed 
after linking data. 
 
With the explanations provided above, we hope that the ethical review committee 
will be kind enough to approve our request to waive the informed consent 
requirement for this study using medical records. As we have mentioned above, the 
study is of potentially high impact and unfortunately not feasible without the 
waiver.