Development and Evaluation of an Artificial Intelligence Model for Cervical Cancer Detection From Colposcopic Images
Conditions: Uterine Cervical Neoplasms
Sponsor: Bangladesh University of Engineering and Technology

Research Protocol: 
Development and Evaluation of an Artificial 
Intelligence Model for Cervical Cancer Detection from 
Colposcopic Images 
Principal Investigator: 
Dr. Taufiq Hasan 
Associate Professor, Biomedical Engineering, 
Bangladesh University of Engineering and Technology (BUET), Dhaka – 1205. 
Co-Principal Investigator: 
Prof. Dr. Nasrin Ara Zaman, MBBS, DGO, FCPS (Obs & Gynae)  
Obstetrics & Gynecology Specialist & Surgeon  
Ibn Sina Medical College Hospital, 1/1-B, Kallyanpur, Mirpur, Dhaka-1216. 
Date of Submission 
12 / 20 / 2023

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: Prof. Dr. Nasrin Ara Zaman 
Qualification: MBBS, DGO, FCPS(Obs & Gynae) 
Detail Address: Ibn Sina Medical College Hospital, 1/1-B, Kallyanpur, 
Mirpur, Dhaka-1216 
Mobile: 01715031998 
     Telephone (Off./Res): N/A 
e-mail: N/A
3.
Place of the Study/Institution(s):
i.
Ibn Sina Medical College Hospital, Kallyanpur
1/1, Mirpur Road, Kallyanpur, Dhaka-1216
ii.
Ibn Sina Diagnostic & Imaging Center, Dhanmondi
House 48, Road 9/A, Dhanmondi, Dhaka-1209
Other national/international medical institutes if needed. 
4.
Title of Study: Development and Evaluation of an Artificial
Intelligence Model for Cervical Cancer Detection from Colposcopic
Images
5.
Type of Study: Prospective and retrospective case-control study.
6.
Duration of Study: 6 months

Page | 2 
7.
Total Cost: N/A
8.
Funding Agency: N/A.
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:  12/20/2023  
_______________________________________________________________ 
Prof. Dr. Nasrin Ara Zaman, MBBS, DGO, FCPS (Obs & Gynae) 
Obstetrics & Gynecology Specialist & Surgeon  
Ibn Sina Medical College Hospital, 1/1-B, Kallyanpur, Mirpur, Dhaka-1216 
Date:  12/20/2023  

 
 
 
 
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  
Yes  
No  
      
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 subje

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RESEARCH PROTOCOL 
●
Project Title: Development and Evaluation of an Artificial Intelligence
Model for Cervical Cancer Detection from Colposcopic Images
Summary: Cervical cancer currently poses a significant health threat due
to late-stage diagnosis leading to higher mortality rates and limited access
to timely screenings, especially in underserved populations. In context,
colposcopy is a screening method to visually examine the cervix, vagina and
vulva using a colposcope for signs of cancerous or precancerous cervix tissue.
Colposcopy, which is used for further examination of women with
abnormalities from the cytology or HPV test, is based on the interpretation
of cervical images from colposcope [1]. Three kinds of cervical images are
used for visual inspection, including the saline image, acetic acid image and
iodine image [2]. Although official organizations have released standards and
quality control for colposcopy practice [3], [4], [5], the diagnoses are mainly
dependent on the experience of doctors, which is subjective and has poor
repeatability [6]. Additionally, it was reported that the visual inspection was
inaccurate, even by experienced doctors. The gold standard for the diagnosis
of cervical lesions is colposcopy-directed biopsy with histopathological
confirmation, however, it is invasive and may cause complications such as
bleeding or infection. Thus, an objective, and accurate cervical screening
approach based on existing clinical examinations is needed.[7]
As traditional vision screening from colposcopy has risks of misdiagnosis and
has low accessibility for time and personnel constraints, the primary focus is
on leveraging advanced computational techniques to automate the analysis
of colposcopy images, addressing the limitations inherent in traditional
vision screening approaches.
By utilizing deep learning methods, we aim to create a model that surpasses
the accuracy of vision assessments, providing a reliable and efficient means
of identifying transformation zones and detecting early signs of cervical
abnormalities.
Central to the aim of this research is the recognition of the pressing need for
scalable and cost-effective solutions in low-income settings. The proposed
deep learning model seeks to mitigate the shortage of trained personnel by
automating the intricate process of colposcopic image analysis. This not only
streamlines the screening process but also enables its widespread
implementation, making cervical cancer screening more accessible to a larger
population.
The research further aims to incorporate domain adaptation techniques, to
ensure that the developed model is not only accurate but also robust across
different health facilities, accounting for the unique challenges posed by
resource constraints, equipment variations, and demographic diversity.
●
Introduction: Colposcopy is a medical diagnostic procedure to visually
examine the cervix as well as the vagina and vulva using a colposcope. The
main goal of colposcopy is to prevent cervical cancer by detecting and treating
precancerous lesions early. Human Papillomavirus (HPV) is a common
infection and the underlying cause for most cervical cancers.
Globally, cervical cancer is the fourth most common cancer in women, with
604,000 new cases in 2020. About 90% of the 342,000 deaths caused by

Page | 5 
cervical cancer occurred in low- and middle-income countries. The highest 
rates of cervical cancer incidence and mortality are in sub-Saharan Africa 
(SSA), Central America and South-East Asia. Regional differences in the 
cervical cancer burden are related to inequalities in access to vaccination, 
screening and treatment services, risk factors including HIV prevalence, and 
social and economic determinants such as sex, gender biases and poverty.[8] 
In Bangladesh, cervical cancer is the second most common cancer of females 
(12%). The number of new cases was 8,068 (10.6 per 100,000 women) and 
deaths was 5.214 (7.1 per 100,00 women) in 2018.[9] The prediction was that 
without any intervention a total of 505,703 women in Bangladesh will die 
from cervical cancer by the year 2070 and the number will rise to 1,042,859 
by 2120.[10] 
For cervical cancer screening, women are referred to colposcopy for diagnosis 
and management. Typically, colposcopy results are characterized by a 
colposcopic impression and by selection of the worst-appearing site for 
biopsy.[11] The biopsy result determines whether treatment is required by 
excision of the transformation zone. In addition, women with persistent 
abnormalities after colposcopy and women undergoing surveillance after 
treatment are managed with colposcopy.[12] 
Current research works on employing artificial intelligence models for 
colposcopy evaluation addresses the need for an automated system to aid 
practitioners in diagnosing cervical cancer and to provide proper treatment 
in low resource settings [13]. Such automated systems focus on tasks such as 
improvement of cervix image quality, regional segmentation, identification 
of unstable regions, transformation zone type classification (TZ) and cancer 
risk classification [14]. For classification tasks, CNN, SVM classifier, 
Transfer Learning, Ensemble Learning methods [14][16][17] and for regional 
segmentation tasks, logical algorithms, Mask-R CNN, UNet, Yolo models are 
mostly employed in present studies [13][15][18]. 
Datasets used in current works are mostly collected from local hospitals and 
annotated by medical professionals, as publicly available datasets are scarce 
and without proper cancer diagnosis annotation [19]. In present studies, 
there is a lack of validation of proposed models on datasets from different 
facilities and regions. This imposes a question on the generalizability of the 
proposed models and their diagnostic efficiency in clinical use with 
diversified data. 
Our research aims to address this generalizability of cervical cancer classifier 
AI models where datasets from different facilities are evaluated, and proper 
domain adaptations are employed for improving the diagnostic performance 
of the models on a diverse range of data. 
●
Objectives: The primary objective is to curate a comprehensive dataset of
colposcopy 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 automated
classification of transformation zones and detection of cervical cancer within
colposcopy images.
The specific objectives of the study are:

Page | 6 
a) Evaluate the artificial intelligence model's performance through clinical
validation using a diverse dataset. Measure sensitivity, specificity, and
accuracy in classifying cancer stages and identifying the transformation
zone in colposcopy images.
b) Assess the usability of the developed model by involving healthcare
professionals in the evaluation process. Measure the ease of integration
into existing clinical workflows, interpretability of results, and the time
efficiency of the model in aiding colposcopy examinations.
c)
Investigate the impact of the artificial intelligence model on early
detection of cervical cancer. Analyze how the model contributes to
identifying precancerous lesions and transformation zones at an earlier
stage, potentially leading to improved patient outcomes.
d) Evaluate the model's generalizability across diverse datasets, including
local data sources. Measure the performance on data from different
regions and healthcare settings to ensure the model's applicability in
various clinical scenarios.
e) Investigate the impact of the deep learning model on patient outcomes,
such as reduced time to diagnosis, increased accuracy in identifying
cancer stages, and improved treatment planning. Assess the potential
positive effects on patient well-being and satisfaction
f)
Evaluate the model as a clinical decision support tool. Measure its
effectiveness in aiding healthcare professionals in making informed
decisions during colposcopy examinations, ultimately contributing to
more accurate diagnoses and treatment plans.
●
Rationale: The existing challenges in cervical cancer screening in
developing countries like Bangladesh stem from the inherent inaccuracies
associated with vision screening methods and the scarcity of trained
personnel for comprehensive colposcopic examinations.[21]
Vision screening, although a common initial approach in resource-limited
settings, is marked by a high rate of false negatives and positives, leading to
potential delays in the identification of cervical abnormalities or unnecessary
distress for patients. Moreover, the shortage of skilled healthcare
professionals capable of conducting colposcopic examinations further
compounds the problem, limiting the reach and efficacy of cervical cancer
screening programs.
This research aims to bridge these critical gaps by using deep learning for
the automated detection of cervical cancer in colposcopy images. The
development of a sophisticated deep learning model holds the potential to
revolutionize cervical cancer screening in low-income countries. By
leveraging a diverse dataset collected from various health facilities within
these regions, the model will be trained to accurately identify transformation
zones and detect signs of cervical abnormalities with a level of precision that
surpasses traditional vision screening methods.
The significance of this research lies in its potential to provide an accessible
and cost-effective solution for cervical cancer screening. The automation of
image analysis through a deep learning model mitigates the dependence on
human expertise, addressing the shortage of skilled personnel in resource-
limited environments. This approach not only enhances the accuracy of
detection but also facilitates the scalability of cervical cancer screening
initiatives, making them more feasible and sustainable in the face of
constrained healthcare resources.

Page | 7 
●
Methodology: In the clinic-based approach for colposcopic cervical cancer
screening data collection, information will be directly obtained from patients
within healthcare facilities to develop an AI algorithm for automatic cancer
detection. The process involves capturing necessary metadata and clinical
details, including colposcopic images. Utilizing clinic resources, the collected
data will be utilized to train the AI algorithm, providing insights into the
likelihood of abnormal findings during the screening. Importantly, the
methodology prioritizes privacy by excluding personally identifiable
information in the clinic-based data collection process.
a) Study Population: The study is structured as a prospective and
retrospective case-controlled investigation aimed at developing and
evaluating an AI-based diagnostic tool for the detection of cervical
cancer by visual inspection. The target patient groups will consist of
confirmed cervical cancer patients diagnosed through gold standard
tests and individuals presenting with cervical abnormalities
resembling cancer (such as cervical dysplasia, human papillomavirus
infection, or other precancerous lesions), with corresponding
confirmatory test results. Additionally, data from individuals with
suspected cervical cancer (test status unknown) or normal cervical
condition will be included to facilitate unsupervised learning. The
primary objective is to evaluate the algorithm's capability to
differentiate between these categories of patients through the
analysis of their medical image data.
▪
Inclusion criteria:
●
Female patients of age 18 years or older can be selected
as subjects.
●
Individuals willing to participate in cervical cancer
screening.
●
Availability for colposcopic examination.
●
Women with no history of hysterectomy (total removal
of the uterus).
●
Women with no current or prior diagnosis of cervical
cancer.
●
Availability 
of 
relevant 
medical 
records 
for
confirmation and comparison purposes.
▪
Exclusion criteria:
●
Pregnant women, given the potential impact on
screening 
results 
and 
the 
need 
for 
special
considerations during pregnancy.
●
Individuals 
with 
severe 
medical 
conditions 
or
circumstances that may make colposcopic examination
inappropriate or unsafe.
●
Patients with conditions that could interfere with the
accuracy of the screening results, such as severe
vaginal bleeding.
●
Follow-up screenings.
b) Sample size calculation: Following the methodology of sample size
calculation to evaluate diagnostic performance (for algorithms in this

Page | 8 
case), we use the following method of calculation for a target 
sensitivity and specificity incorporating disease prevalence [22]:  
𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠≥ 
𝑍𝑍2(1−𝛼𝛼)/2 × 𝑠𝑠𝑠𝑠𝑠𝑠 (1 −𝑠𝑠𝑠𝑠𝑠𝑠)
𝑐𝑐2 × 𝑝𝑝
𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠≥ 
𝑍𝑍2(1−𝛼𝛼)/2 × 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (1 −𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)
𝑐𝑐2 × (1 −𝑝𝑝)
where, 
𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠 
= 
Required sample size for sensitivity, 𝑠𝑠𝑠𝑠𝑠𝑠  
𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 
= 
Required sample size for specificity, 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 
𝛼𝛼 
= 
Confidence level 
𝑍𝑍2(1−𝛼𝛼)/2 = 
The Z-score for the confidence level 𝛼𝛼 
𝑝𝑝 
= 
Prevalence of cervical cancer 
𝑐𝑐 
= 
Confidence interval 
𝑠𝑠𝑠𝑠𝑠𝑠 
= 
Estimated sensitivity 
𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 
= 
Estimated specificity 
The final sample size 𝑁𝑁 is calculated based on the larger of the two 
values 𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠 and 𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠. In this study, as we are using deep learning 
techniques, we need the medical image samples to reflect the various 
cancerous, precancerous and normal cases. For our analysis, we 
assume an 80% confidence level (𝛼𝛼= 0.80) and prevalence of cervical 
cancer for self-reported instances, 𝑝𝑝 = 0.261 [23]. A confidence interval 
of 5% is selected (𝑐𝑐 = 0.05). We estimate the sensitivity and specificity 
of our algorithms to be at 75% (𝑠𝑠𝑠𝑠𝑠𝑠= 0.75 and 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠= 0.75). Using 
these values, we obtain the sample size values: 𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠=   471  and 
𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠=  166. Since 𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠> 𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠, the final sample size for the study will 
be: 
𝑁𝑁= 𝑁𝑁𝑠𝑠𝑠𝑠𝑠𝑠= 471 
We can set a target of N = 500. 
c)
Procedures: In the prospective phase of the study, data collection
will be conducted within clinical settings. Eligible patients will be
approached during clinic visits 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
administer colposcopic examinations, and images of the cervical
region will be captured using specialized equipment available in the
clinic. 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
colposcopic cervical cancer screening.

Page | 9 
In the retrospective phase of the study, additional data will be 
extracted from the clinic's database of previous screenings. 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 case-control study:
▪
Patient ID (unique identification number)
▪
Date & Time of the data input
▪
Age
▪
Patient’s colposcopic image (can be multiple images from
different stages of test – normal, acetic acid, iodine etc.)
▪
HPV status
▪
Transformation zone type
▪
Lesion size
▪
Provisional diagnosis
▪
Suggested management (e.g. routine screening, biopsy etc.)
▪
Histopathology
▪
Patient’s relevant medical/surgical history
The following information will also be collected if available: 
▪
Relevant diagnostic test results
▪
Swede score
The cervix images collected from colposcopic assessment of the 
subjects along with the relevant metadata will immensely benefit in 
designing the algorithms and analysis of the images. 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 will be initiated to examine the distribution
of patients across various parameters such as transformation zone
type, histopathology and relevant clinical factors. Following this, a
balanced 
dataset 
will 
be 
curated, 
ensuring 
an 
equitable
representation of image samples.
This dataset will form the basis for training and evaluating the
artificial intelligence (AI) algorithm developed for the automatic
classification and detection of abnormalities in colposcopic cervical
screening cases. The trained model will provide predictions on the
presence or absence of abnormalities in the cervical images.
To validate the accuracy and clinical relevance of the algorithm's
predictions, the results will undergo verification and analysis in
collaboration with expert colposcopists. Their input will be

Page | 10 
instrumental in refining the automatic algorithm, enhancing its 
precision in the classification and detection of potential abnormalities 
during colposcopic cervical cancer screenings. 
f)
Statistical Analysis: Upon dataset availability, statistical analysis
will focus on assessing the performance of the developed artificial
intelligence algorithms for automatic classification and detection of
abnormalities in colposcopic cervical screening cases. Utilizing
metrics such as sensitivity, specificity, precision, recall, F-1 score, and
overall accuracy, the algorithms will be rigorously evaluated. To
ensure robust results, experiments with n-fold cross-validation will be
conducted, and mean values along with standard deviations of
performance metrics will be calculated. The analysis will culminate
in a comparative assessment of different deep learning models,
gauged for their effectiveness in the automatic classification task.
Utilization of Results: The study results will play a crucial role in the 
development and evaluation of an automatic detection algorithm for cervical 
cancer screening. The algorithm's primary objectives are: 
a) Automatic Detection: Assess and identify potential abnormalities
in colposcopic images, providing an automated screening tool for
efficient detection of cervical cancer risk.
b) Clinical Decision Support: Assist healthcare professionals by
offering an automated analysis that aids in prioritizing treatment
strategies and making effective clinical decisions based on the
algorithm's findings.
c)
Systematic Data Storage: Facilitate systematic storage of
colposcopic images and relevant metadata to support ongoing
research initiatives, all while ensuring the privacy of individuals
during the automatic detection process.
These outcomes are poised to significantly enhance the efficiency and 
accuracy of cervical cancer screening, providing valuable support to 
healthcare professionals in Bangladesh. 
●
Facilities: In the case of collecting data from colposcopic cervical cancer
screening, no additional facilities are required except those the clinics are
already equipped with (e.g. image acquisition and data storage capacity).
●
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.

Page | 11 
●
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 and metadata 
3 
Homogenization and organization of data 
3 
Data processing and analysis 
●
Ethical Implications: The proposed retrospective/prospective case-control
study based on medical records is designed to develop and evaluate the
diagnostic performance of Artificial Intelligence (AI) algorithms that will
detect cervical cancer stage from colposcopic images. The study poses
minimal risk to the subjects according to the HHS (USA) definition.
●
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Page | 14 
PART - C 
INFORMED CONSENT FORM (PROSPECTIVE STUDY) 
Title: Development and Evaluation of an Artificial Intelligence Model for Cervical 
Cancer Detection from Colposcopic Images 
Principal Investigator: 
Dr. Taufiq Hasan, Associate Professor, 
Department of Biomedical Engineering, BUET, Dhaka – 1205
Please check all that are applicable: 
☐I am an adult participant in this study.
☐I am the parent or guardian granting permission for a child in this study (the use
of "you" refers to "your child" or “your ward.”)
Are you participating in any other research studies? (Yes/No) _____ 
PURPOSE OF PROJECT 
You are invited to participate in a project entitled Development and Evaluation 
of an Artificial Intelligence Model for Cervical Cancer Detection from 
Colposcopic Images. This project focuses on developing an algorithm that will 
automatically detect cervical cancer or precancerous stages using colposcopy images. 
The period of the study will be between November 1, 2023 to January 31, 2024. 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.   

Page | 15 
PROCEDURES 
The clinic-based approach involves directly obtaining information during clinical 
visits for developing an automatic cancer detection AI algorithm. No personally 
identifiable data is collected, ensuring privacy. Eligible female patients (age 18 or 
older) without prior cervical cancer diagnosis will provide essential clinical 
information during colposcopic examinations. In the retrospective phase, data from 
previous screenings will be extracted, ensuring privacy standards. The recorded 
data includes patient ID, colposcopic images, HPV status, and relevant medical 
history. The collected dataset will train the AI algorithm for automatic classification 
and detection, emphasizing patient privacy throughout the process. 
THE PROCEDURE IS MINIMALLY 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: 
●
Adhere to instructions provided by healthcare professionals during clinic
visits for data collection.
●
Contact the data collection coordinator or research study staff promptly if
you have any questions or concerns.
●
If you are a healthcare professional, particularly a colposcopist, ensure
accurate maintenance of the image ID to prevent data mix-ups.
●
Notify the coordinator or research staff directly if you decide to withdraw
from the study on the clinic-based system, ensuring seamless communication
with the study staff.
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 ABOUT YOUR DISSENT WITHIN THE PERIOD OF 
THE STUDY BEING CONDUCTED. 

Page | 16 
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.
POSSIBLE RISKS, DISCOMFORTS, AND INCONVENIENCES 
There may be mild discomfort during the procedure and, in rare cases, minimal 
bleeding or infection at the biopsy site. 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 
cervical cancer. 
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. 
May we contact you about future studies that may be of your interest? (Yes/No) 
Checking on the box with a click means you agree to be in this study.  
☐Agreed

Page | 17 
JUSTIFICATIONS FOR INFORMED CONSENT WAIVER 
(RETROSPECTIVE STUDY) 
The proposed research is a case-control study that only uses medical records of 
colposcopy. As the number of samples required is large (500 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 justifications 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. 

Page | 18 
2. The research could not be carried out practicably without the waiver or
alteration. Training artificial intelligence algorithms require 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 not practically
possible to collect signed or verbal informed consent from each patient from each
hospital. In addition, most of the patients hospitalized due to COVID-19 are in
severe conditions and are not capable of providing consent.
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
at the source.
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 case-control study using medical records. As we have 
mentioned above, the study is of potentially high impact and unfortunately not 
feasible without the waiver.