Z 32104 - Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age
Conditions: Gestational Age, Machine Learning, Pregnancy Related
Sponsor: University of North Carolina, Chapel Hill

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
Diagnostic accuracy of a novel machine learning algorithm to estimate gestational age 
 
 
Protocol Version 1.3 
25 May 2023 
 
 
 
 
Funded by: 
The Bill and Melinda Gates Foundation 
OPP 1191684 
 
 
 
 
 
 Principal Investigators: 
Jeffrey S. A. Stringer, MD, FACOG 
Bellington Vwalika, MBChB, MMed, MSc 
 
 
Study Registration: 
ClinicalTrials.gov 
NCT05433519   
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
NB: This protocol is organized and formatted according to the guidelines set forth by the 
University of Zambia Biomedical Research Ethics Committee (UNZA BREC) 
 
 

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
Protocol Signature Page 
Protocol Title:  
Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate 
Gestational Age  
Short Title:  
FAMLI Diagnostic Accuracy 
Protocol Version:  
1.3 
Version Date: 
25 May 2023 
 
I, the Investigator of Record, agree to conduct this study in full accordance with the provisions of 
this protocol. I will comply with the provisions of this protocol, all requirements regarding the 
obligations of clinical investigators as fully outlined in the International Conference on 
Harmonization (Section E6(R1) Good Clinical Practice), local regulatory requirements, and the 
Investigator’s Agreement, which I have also signed. 
 
I have read and understand the information in the Investigator's Brochure(s), including the 
potential risks and side effects of the product under investigation, and will ensure that all staff 
assisting in the conduct of the study are informed about the obligations incurred by their 
contribution to the study. 
 
 
Investigator of Record Name:  
 
 
 
 
 
 
 
 
 
Investigator of Record Signature:  
 
 
 
 
 
 
 
 
 
Date:   
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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Protocol v1.3 | 25 May 2023 
TABLE OF CONTENTS 
INVESTIGATORS AND SCIENTIFIC TEAM .......................................................................................... 4 
ABBREVIATIONS AND ACRONYMS .................................................................................................. 5 
PROTOCOL SUMMARY .................................................................................................................... 6 
1.0 
Introduction .................................................................................................................... 7 
2.0 
Statement of the Problem ............................................................................................... 7 
3.0 
Rationale ......................................................................................................................... 8 
4.0 
Background and Literature Review ................................................................................. 8 
4.1 
Preliminary Data ........................................................................................................ 10 
5.0 
Research Aims ............................................................................................................... 11 
5.1 
Primary objective ....................................................................................................... 12 
5.2 
Secondary objectives ................................................................................................. 12 
6.0 
Study Methodology ....................................................................................................... 12 
6.1 
Study design .............................................................................................................. 12 
6.2 
Study sites and study population .............................................................................. 13 
6.3 
Study visit procedures ............................................................................................... 13 
6.4 
Retention ................................................................................................................... 15 
6.5 
Safety monitoring ...................................................................................................... 15 
6.6 
Biological specimen collection ................................................................................... 17 
6.7 
Data security and management ................................................................................ 17 
6.8 
Statistical considerations ........................................................................................... 18 
6.9 
Dissemination of findings .......................................................................................... 21 
7.0 
Ethical Considerations ................................................................................................... 21 
7.1 
Informed consent ...................................................................................................... 22 
7.2 
Potential risks to participants .................................................................................... 22 
7.3 
Potential benefits to participants and others ............................................................ 23 
7.4 
Inclusion of children, sub-populations, and vulnerable populations ......................... 23 
8.0 
Timeline ......................................................................................................................... 23 
9.0 
Budget  .......................................................................................................................... 23 
10.0 
Reference List ................................................................................................................ 25 
 
 
 

 
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INVESTIGATORS AND SCIENTIFIC TEAM 
Principal Investigators: 
Jeffrey S. A. Stringer, MD, FACOG  
Professor of Obstetrics and Gynecology 
UNC School of Medicine 
Campus Box 7570, Chapel Hill, NC 27599 
Email: jeffrey_stringer@med.unc.edu 
 
Bellington Vwalika, MBChB, MMed, MSc 
Professor and Chair 
Department 
of 
Obstetrics 
and 
Gynaecology 
University of Zambia School of Medicine 
Lusaka, Zambia 
Email: vwalikab@gmail.com  
 
Co-Investigators: 
Margaret Kasaro, BSc, MBChB, MSc 
Country Director 
UNC Global Projects Zambia 
338 Independence Ave, Lusaka, Zambia 
Email: margaret.kasaro@unclusaka.org 
 
Nelly Mandona, MBChB 
Research Medical Officer  
UNC Global Projects Zambia 
338 Independence Ave, Lusaka, Zambia 
Email: nelly.mandona@unclusaka.org 
 
Elizabeth M. Stringer, MD, MSc 
Associate Professor of Obstetrics and 
Gynecology, Division of Maternal-Fetal 
Medicine 
UNC School of Medicine 
Campus Box 7570, Chapel Hill, NC 27599 
Email: elizabeth_stringer@med.unc.edu 
 
 
 

 
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ABBREVIATIONS AND ACRONYMS 
AC 
 
 
Abdominal circumference 
ACOG  
 
American College of Obstetricians and Gynecologists 
AI 
 
 
Artificial intelligence 
ANC 
 
 
Antenatal care 
BMGF  
 
Bill and Melinda Gates Foundation 
BOE 
 
 
Best obstetric estimate 
CNN 
 
 
Convolutional Neural Network 
DICOM 
 
Digital Imaging and Communications in Medicine 
EDD 
 
 
Estimated date of delivery 
FAMLI  
 
Fetal Age Machine Learning Initiative  
FH 
 
 
Fundal Height 
FWA 
 
 
Federal wide assurance 
GA 
 
 
Gestational age 
hCG 
 
 
Human chorionic gonadotropin hormone 
IVF 
 
 
In vitro fertilization 
LH 
 
 
Luteinizing hormone 
LMIC  
 
Low- and middle-income countries 
LMP 
 
 
Last menstrual period 
MAE 
 
 
Mean absolute error 
ML 
 
 
Machine Learning 
MUAC  
 
Mid-upper arm circumference 
OHRP  
 
US Office for Human Research Protections  
PHI 
 
 
Protected Health Information 
PI 
 
 
Principal investigator 
SAE 
 
 
Serious adverse event 
SOP 
 
 
Standard operating procedure 
UNC 
 
 
University of North Carolina 
UNZA  
 
University of Zambia 
US 
 
 
Ultrasound 
UTH 
 
 
University Teaching Hospital 
WHO  
 
World Health Organization  
 
 

 
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Protocol v1.3 | 25 May 2023 
PROTOCOL SUMMARY 
Background: 
Most of the world’s obstetrical providers do not have access to the 
diagnostic benefit of ultrasound because of the historically high cost of 
equipment and need for trained sonographers. We have developed a 
machine learning tool (“FAMLI Technology”) that can estimate fetal 
gestational age from a series of ultrasound blind sweeps collected by 
an untrained operator using a low-cost, battery-powered device. 
Objectives:  
The overall aim of this study is to assess the diagnostic accuracy of a 
machine learning algorithm in estimating gestational age in the second 
and third trimesters of pregnancy. The primary study outcome is 
diagnostic accuracy, measured as the difference in mean absolute error 
(MAE) of the FAMLI Technology and standard fetal biometry in the 
primary evaluation window.  
Design:  
This is a prospective cohort study of women enrolled early in 
pregnancy, with randomization to determine the timing of three 
follow-up visits in the second and third trimester. At each of these 
follow-up visits, we will assess gestational age with the FAMLI 
technology and compare that estimate to the known gestational age 
established early in pregnancy. We will also conduct standard 
ultrasound biometry measurements likewise assessing the accuracy of 
this approach. Our study design will allow precise characterization of 
the technology’s accuracy (against the previously established ground 
truth) and also allow head-to-head comparison of the FAMLI 
Technology to standard fetal biometry. 
Population:  
400 pregnant women aged 18 or older enrolled before 14 weeks of 
gestation as documented by a baseline ultrasound scan and followed 
through delivery. All participants must have a viable, singleton, 
intrauterine pregnancy.  
Study Sites:  
This study will be conducted in the antenatal clinics of the University 
Teaching Hospital (UTH) and the Kamwala District Health Centre in 
Lusaka, Zambia and the prenatal clinics of the University of North 
Carolina at Chapel Hill.  
Duration/Follow up: Participants will be enrolled over approximately one year and followed 
through delivery. The study is expected to take two years to complete.  
Relevance:  
Among the most critical benefits of obstetric sonography is its ability to 
measure anatomical structures within the fetus and from this, make an 
estimate of fetal gestational age. Obstetric care providers offer a well-
established set of screening and preventative activities as pregnancy 
proceeds, and the timing of many of these relies upon accurate 
estimation of the GA. Given the importance of GA to guide appropriate 
clinical care, accurate measurement is critically necessary to improve 
pregnancy outcomes worldwide. 
 
 

 
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1.0 
Introduction 
A lynchpin of modern pregnancy care, obstetrical sonography has been in routine use in North 
America and Europe since the 1970s. Ever-advancing technology makes it now possible to fit 
into a pocket-sized unit the imaging technology that once would have required a 200-pound 
machine on casters. Yet, because of the historically high cost of equipment and need for 
trained sonographers, most of the world’s obstetrical providers do not have access to the 
diagnostic benefit of ultrasound. Without it, they are armed only with centuries-old physical 
exam techniques that do not provide sufficient diagnostic discrimination to identify and 
appropriately refer most high-risk cases. 
In 2018, our team received funding from the Bill and Melinda Gates Foundation to generate 
data, engineering, and analytical solutions to contribute to the development of robust, 
inexpensive, ultrasound technologies that could be deployed in low-resource settings to 
improve obstetric diagnostics. The first phase of that project – the Fetal Age Machine Learning 
Initiative (FAMLI) protocol (UNZA BREC Ref 005-08-18, UNC IRB #18-1848) – has enrolled 
nearly 5,000 women in Zambia and North Carolina into a prospective data collection protocol. 
We have analyzed data from these participants and built a tool that can estimate gestational 
age (GA) from blindly obtained ultrasound sweeps of the gravid abdomen. This innovation 
takes advantage of two new technologies. The first is the miniaturization of medical 
ultrasound technology. Over the last decade, several low-cost, handheld, ultrasound devices 
have become available – many of which have obtained U.S. Food and Drug Administration 
(FDA) 510(k) clearance and CE conformity marking in the European Union.1,2 The second new 
technology is the application of machine learning (ML) computer vision models for automated 
medical image interpretation.3,4 
This protocol describes a diagnostic accuracy study to assess the performance of our new tool 
(hereinafter “FAMLI Technology”) in assessing gestational age. We will enroll 400 pregnant 
volunteers prior to 14 completed gestational weeks and obtain accurate “ground truth” GA 
dating with standard ultrasound biometry, using the crown-rump length. These participants 
will then be asked to return for three follow-up visits, which will include a routine sonogram 
performed by a trained sonographer and the collection of a set of blind sweep cineloop videos 
using a low-cost, battery-operated device. The research will be conducted in Chapel Hill, 
North Carolina (at the University of North Carolina Hospital and/or sites associated with UNC 
OBGYN) and in Lusaka, Zambia (at the University Teaching Hospital or Kamwala District Health 
Centre). Approximately equal numbers of participants will be enrolled from each country.  
NB: The primary purpose of this research is to assess the diagnostic accuracy of the FAMLI 
Technology, a novel machine learning-based tool for gestational age assessment that can 
run on a smart phone or tablet. As such, we are not evaluating the performance of any 
ultrasound device and this protocol will only use ultrasound scanners that are approved for 
use in the participating countries. 
2.0 
Statement of the Problem 
Approximately 830 women die each day worldwide from preventable causes related to 
pregnancy and childbirth, and almost all of these deaths occur in low-resource settings.5,6 
Approximately 2.7 million neonates die per year,7 and an additional 2.6 million are stillborn.8 
Early ascertainment of fetal gestational age is critical to good clinical management, as is 
detection of clinical obstetric conditions that put both mothers and fetuses at increased risk 
of morbidity and mortality. Ultrasound improves GA estimation and can provide detailed 

 
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information on fetal wellbeing and maternal conditions, thus identifying high-risk pregnancies 
and enabling informed decisions around patient management (Table 1). Despite these well-
described benefits, obstetric sonography is not available to the majority of the world’s 
pregnant women.9  
Table 1: Obstetrical diagnoses facilitated by sonography 
Diagnosis 
Proportion of 
pregnancies 
affected 
Adverse maternal outcomes 
Adverse fetal or neonatal 
outcomes 
Ectopic pregnancy 
0.01510 
Hemorrhage, death 
- 
Twin or triplet gestation 
0.01311 
Preeclampsia, hemorrhage, 
death 
Prematurity, perinatal death 
Placenta previa, accreta, 
vasa previa 
0.00512 
Hemorrhage, death 
Anoxic brain injury, perinatal 
death 
Fetal malpresentation 
0.03813 
Obstructed labor, fistula, pelvic 
trauma, sepsis, hemorrhage, 
death 
Anoxic brain injury, perinatal 
death 
Fetal growth restriction 
0.1014 
- 
Anoxic brain injury, perinatal 
death 
Macrosomia (> 4,000g) 
0.0815 
Obstructed labor, fistula, pelvic 
trauma, sepsis, hemorrhage, 
death 
Birth trauma, peripheral 
nerve injury, anoxic brain 
injury, perinatal death 
Oligohydramnios 
0.1116 
- 
Perinatal death 
Polyhydramnios 
0.0117 
Respiratory compromise, 
hemorrhage, death 
Prematurity, perinatal death 
Pre-term birth 
0.11118 
- 
Prematurity, neonatal death 
Post-term pregnancy 
0.004-0.0719 
Obstructed labor, fistula, pelvic 
trauma, sepsis 
Anoxic brain injury, 
meconium aspiration 
syndrome, perinatal death 
Intrauterine fetal death 
0.018420 
Coagulopathy, sepsis, 
hemorrhage, death 
- 
Retained placenta 
0.0221 
Sepsis, hemorrhage, death 
- 
 
3.0 
Rationale 
Among the most critical benefits of obstetric sonography is its ability to measure anatomical 
structures within the fetus and from this, make an estimate of fetal gestational age. Obstetric 
care providers offer a well-established set of screening and preventative activities as 
pregnancy proceeds,22 and the timing of many of these relies upon accurate estimation of the 
GA. Similarly, if a complication arises during routine care, an accurate gestational age 
estimate is an essential piece of information required to make appropriate decisions around 
the clinical management of mother and fetus. Providers may make drastically different 
decisions, for instance, in the care of a woman who develops a pregnancy complication prior 
to fetal viability compared to that same complication developing at term. Given the 
importance of GA to guide appropriate clinical care, accurate measurement is critically 
necessary to improve pregnancy outcomes worldwide. 
4.0 
Background and Literature Review 
By convention, pregnancies are dated from the first day of the last normal menstrual period 
(LMP). Conception typically happens 14 days later, but this period can vary substantially 
among individuals (Figure 1).23 Thus, we rarely know the exact date of conception. A notable 
exception includes in vitro fertilization (IVF), where we know the exact date of fertilization 

 
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and thus the age of the embryo on the date of transfer 
to the uterus. Among women who monitor ovulation 
with luteinizing hormone (LH) test strips, we can 
estimate the date of conception within 1-3 days. A urine 
pregnancy test, which detects human chorionic 
gonadotropin hormone (hCG) in the urine, typically 
becomes positive 2 weeks after conception (i.e., 4 
weeks GA by convention).  
Gestational age dating in settings where obstetric 
ultrasound is not routinely available relies primarily 
upon the LMP. This is typically ascertained through 
patient interview at the first antenatal visit. An 
estimated date of delivery (EDD) is assigned by adding 
280 days (40 weeks) to the first day of the recalled LMP 
(Naegele’s rule).24 While this approach is simple and 
cost-effective, it is well known that LMP recall call be 
inaccurate,25 
especially 
in 
settings 
where 
first 
presentation to antenatal care is later in gestation.26 
Indeed, data on over 100,000 pregnancies in Lusaka, 
Zambia indicate a median GA at presentation of 23 
weeks.27 Further, data from our research site in Zambia 
suggests that participant recall is not only imprecise, 
but also may introduce bias (Figure 2).26 
Gestational age can also be estimated by physical exam 
and in many settings, this approach is used to 
supplement – and occasionally overrule – estimate 
made by LMP. The height of the uterine fundus 
(“fundal height”) is ascertained with a simple 
flexible tape measure (Figure 3) and is defined as 
the distance between the pubic symphysis and 
the uterine fundus. In the absence of uterine 
pathology (e.g., large leiomyoma) or fetal 
conditions that might cause the uterus to be 
unusually large (e.g., polyhydramnios, multiple 
gestation), the fundal height (in centimeters) 
roughly corresponds to the current gestational 
age (in weeks) once the uterus is palpable above 
the pubic bone.28 
The standard of care for GA estimation in settings where antenatal ultrasound (US) is available 
instead uses an algorithm known as the best obstetric estimate (BOE).24 Typically, an 
ultrasound is performed at the first antenatal visit or soon thereafter. The sonographer 
measures known fetal structures (“biometry”) which are used to estimate the fetal GA on the 
day of the ultrasound. The error associated with current biometry formulae (of which there 
are several29-32) increases with advancing GA.24 
The BOE is calculated by comparing the GA according to LMP to the GA according to 
ultrasound. The reported LMP is used to estimate GA unless the ultrasound measurement 

 
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differs by a predefined amount (Figure 4), in which case the US estimate is used. For women 
whose LMP is uncertain or unknown, the ultrasound estimate alone is used. Obstetrical 
providers generally do not change the estimated due date once it is set by the BOE, since a 
subsequent ultrasound estimate is typically less accurate than an earlier one.  
Figure 4: Gestational age dependent ultrasound measurement error24 
 
4.1 
Preliminary Data 
The FAMLI Technology being evaluated in this protocol is derived from the original FAMLI 
study, which opened to enrollment in September 2018. Between that time and June 2021, 
we collected prospective data from 4,695 participants in Zambia and North Carolina. Women 
were eligible to enroll at any time during pregnancy and could return for repeat scans no 
more frequently than monthly in Zambia and bi-weekly in North Carolina. After provision of 
written informed consent, we collected background clinical and obstetrical information on 
each participant through interview and medical record review. We also documented their 
LMP (if known) and EDD from prior ultrasound (if performed). 
Ultrasound procedures included documentation of fetal viability, pregnancy location, number 
of fetuses, fetal heart rate, fetal lie, and assessment of amniotic fluid volume. Measurements 
of gestational age-appropriate structures for fetal biometry (crown-rump length, abdominal 
circumference, head circumference, bi-parietal diameter, femur length, transcerebellar 
diameter) were each performed twice.  
During the same examination, we also collected a series of blind sweep cineloop videos. These 
were free-hand sweeps, 5 to 10 seconds in length, across the gravid abdomen in multiple 
directions and probe configurations. Cranio-caudal sweeps started at the pubis and ended at 
the level of uterine fundus with the probe indicator facing toward the maternal right either 
perpendicular (90 degrees) or angled (15 and 45 degrees) to the line of probe movement. 
Lateral sweeps were performed with the probe indicator facing superiorly, starting just above 
the pubis and sweeping from the left to the right lateral uterine borders and moving cephalad 
to the uterine fundus. Complete sets of blind sweeps were collected by the study sonographer 
on both the commercial ultrasound machine and a low-cost, battery-powered device 
(Butterfly iQ; Guilford, CT, USA).  
4.1.1 Performance of the deep learning model 
Preliminary results from the novice test set informed the development of the FAMLI 
Technology being assessed in the present protocol. This test set contains 249 participant 
exams in which blind sweeps were obtained by an untrained user on a low-cost, battery-
powered device (Butterfly iQ). We compared the deep learning model estimates to biometry 
obtained by a trained sonographer on a commercial ultrasound (Figure 5). We also compared 

 
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model estimates to the gestational age that would have been calculated had only the LMP 
been available (as is overwhelmingly the case in Zambia and other similar settings where this 
technology would be used). We found that the model and biometry performed similarly: 
overall model mean absolute error (MAE) 4.9 days (standard error [SE] 0.29) versus biometry 
MAE 5.4 days (SE 0.28); difference -0.6 days (95% CI: 1.3, 0.1); p=0.11). However, when 
compared to LMP, the model was clearly superior: model MAE 4.9 days (SE 0.29) versus LMP 
MAE 17.4 days (SE 1.17); difference -12.7 days (95% CI: -15.0, -10.3); p<0.001). Based on the 
empirical cumulative distribution function of the model error, the proportion of study scans 
that were correctly classified within 7 days was substantially higher for the model than for 
LMP (71.9% vs 40.1%; difference 36.1% [95% CI 28.0%, 44.2%]; p<0.001). The model similarly 
outperformed LMP using a 14-day classification window (94.8% vs 55.1%; difference 40.5% 
[95% CI 33.9%, 47.1%]; p<0.001).   
Figure 5: Gestational age estimation of deep learning model compared to trained 
sonographer and last menstrual period (LMP) in the novice test set 
 
a dashed horizontal lines represent expected accuracy of ultrasound biometry4 
b data missing from 22 participants who could not recall their LMP 
c 13 studies from GA by LMP excluded from the plot because the absolute error is truncated at 49 days 
5.0 
Research Aims 
The FAMLI Technology is a machine learning tool that uses a novel deep convolutional neural 
network to estimate fetal gestational age from a series of ultrasound blind sweeps. We intend 
to assess the diagnostic accuracy of the technology by enrolling a cohort of women in early 
pregnancy, establishing their gestational age by crown-rump length measurement, and then 
assigning follow-up visits at random times during three gestational age windows. At each of 
these visits, we will assess gestational age with the FAMLI technology and compare that 
estimate to the known gestational age established at the baseline ultrasound. Alongside this 
assessment, we will also conduct standard ultrasound biometry measurements likewise 
assessing the accuracy of this approach. Our study design will allow precise characterization 
of the technology’s accuracy (against the previously established ground truth) and also allow 
head-to-head comparison of the FAMLI technology to standard fetal biometry (which is the 
current gold standard). 
Terms: 
● Ground truth – the gestational age and corresponding estimated date of delivery 
(EDD) established prior to 14 weeks of gestation and assumed in this protocol to 
represent a gold standard measurement. 
● Blind sweep – freehand ultrasound sweeps of the gravid abdomen collected as 
cineloop video. 

 
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● FAMLI Technology– the trained deep learning model when deployed on an Android 
or iOS device and receiving blind sweep ultrasound data from a low-cost probe (e.g., 
Butterfly iQ+). 
● Untrained operator – a clinician (e.g., nurse or midwife) with general obstetrics 
knowledge but with no prior training in sonography. 
● Index test – a term of art in diagnostic accuracy studies that refers to the test being 
assessed, in this case the FAMLI Technology. 
● Clinical reference standard – a term of art in diagnostic accuracy studies that refers 
to the test against which the index test is evaluated, in this case standard fetal 
biometry performed by a trained sonographer on a commercial ultrasound machine. 
● Primary evaluation window – the GA window corresponding to the second trimester 
of pregnancy (14 0/7 through 27 6/7 gestational weeks, inclusive). At enrollment each 
participant will be assigned a random date within this window to return for a study 
ultrasound evaluation. 
● Secondary evaluation windows – the GA windows corresponding to the preterm third 
trimester (28 0/7 through 36 6/7 weeks, inclusive) and term gestation (37 0/7 through 
40 6/7 weeks, inclusive). At enrollment each participant will be assigned random dates 
within these windows to return for a study ultrasound evaluation. 
5.1 
Primary objective 
● To assess the diagnostic accuracy of the FAMLI algorithm in estimating gestational age 
in the second and third trimesters of pregnancy. This objective will be met by 
comparing the measurement error (defined in Section 6.8.1) of the index test (i.e., the 
FAMLI Technology interpreting blind sweeps obtained on a low-cost probe by an 
untrained operator) to that of the clinical reference standard (i.e., standard fetal 
biometry performed by a trained sonographer on a commercial ultrasound machine). 
5.2 
Secondary objectives 
● To assess the extent to which operator experience affects performance of the FAMLI 
algorithm by comparing diagnostic accuracy of blind sweeps collected by an untrained 
operator to blind sweeps collected by a trained sonographer. 
● To assess the extent to which individual transducer technology affects performance 
of the FAMLI algorithm by comparing diagnostic accuracy of blind sweeps collected on 
a CMUT/CMOS probe to blind sweeps collected on a traditional piezoelectric 
ultrasound probe. 
● To assess the potential of biological metabolites/biomarkers to augment the 
performance of the machine learning model in estimating gestational age  
6.0 
Study Methodology 
6.1 
Study design 
We propose a prospective cohort study of 400 women enrolled before 14 weeks of gestation 
who will undergo 3 follow-up ultrasound evaluations during pregnancy. These follow-up visits 
will be assigned at random dates within 3 specific GA windows corresponding to the second 
trimester, the preterm third trimester, and term gestation (Figure 6). At each study visit, the 
participants will be evaluated with both the FAMLI Technology and standard of care 
ultrasound. We will also collect low-volume blood and urine specimens at each visit for 

 
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Protocol v1.3 | 25 May 2023 
development of a gestational age estimation tool using maternal biomarkers and for future 
protocol-related testing.  
Figure 6: Study visit windows 
 
6.2 
Study sites and study population 
This study will be conducted in the antenatal clinics of the University Teaching Hospital (UTH) 
and the Kamwala District Health Centre in Lusaka, Zambia and the prenatal clinics of the 
University of North Carolina at Chapel Hill.  
Inclusion criteria: 
1. 18 years of age or older 
2. viable intrauterine pregnancy at < 14 0/7 weeks of gestation  
3. ability and willingness to provide written informed consent 
4. intent to remain in current geographical area of residence for the duration of study 
5. willingness to adhere to study procedures 
Exclusion criteria: 
1. maternal body mass index ≥ 40 kg/m2  
2. multiple gestation (i.e., twins or higher order) 
3. major fetal malformation or anomaly 
4. any other condition (social or medical) that, in the opinion of the study staff, would 
make study participation unsafe or complicate data interpretation.  
6.3 
Study visit procedures 
6.3.1 Screening and Randomization 
In Zambia, our recruitment activities will begin with community sensitization in the catchment 
area of the recruitment clinics to educate community members about the study and 
encourage early presentation for ultrasound. This community and clinic sensitization will be 
underway prior to initiation of this protocol as a result of other programs ongoing in the 
catchment areas. In North Carolina, we will advertise the study through flyers, posters, and 
other electronic or written materials. 
Study staff will approach potentially eligible women – either in groups or individually – to 
inform them about the study (including eligibility criteria). Staff will provide interested 
women with additional information and referrals to the study clinic for eligibility assessment. 
All protocol-specified ultrasound procedures will occur after 13 completed gestational weeks 
(i.e., 14 0/7 weeks or greater). To be eligible for study participation, women must have 
documentation of a baseline scan conducted by a trained sonographer prior to 14 weeks 
gestation to confirm pregnancy location, viability, and gestational age by crown-rump length.  
After completing the study informed consent process, study staff will gather targeted 
information on demographics, health and risk behaviors, medical and obstetrical history, 
medication use, stress, and nutrition. Participants will undergo blood pressure assessment 
and anthropometry, including height, weight, mid-upper arm circumference (MUAC), and 
fundal height (FH). Blood and urine will be collected as detailed in Table 3.  
GA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Enroll
Primary Evaluation Window
Secondary Evaluation Windows
< 140
140 to 276
280 to 366
370 to 406

 
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Following final confirmation of eligibility, participant will be assigned a date for each of their 
follow-up visits in a fashion that ensures approximately equal distribution of study visits 
across the GA range within each designated visit window. For instance, the primary evaluation 
window comprises the 14-week period between 14 0/7 and 27 6/7 gestational weeks, inclusive. 
Thus, through the randomization procedure, a participant will have a 1 in 14 chance of being 
allocated to a given gestational week within the primary evaluation window (Table 2). A 
statistician not otherwise associated with the study will design the scheme and pre-generate 
the visit schedules for each participant prior to study commencement. Women will be 
allowed to choose their preferred day of participation within the randomized week.  
Participants who are randomly allocated to a follow-up visit schedule who do not complete 
visit 1.0 for any reason (e.g., because they deliver prior to the target window or do not return 
for the visit) will count as screened but will not count toward the overall enrollment target of 
400 participants.  
Table 2: Random visit allocation 
 
 
6.3.2 Study follow-up 
At each study follow-up visit, research staff will collect interval medical and obstetric history, 
perform anthropometry, measure blood pressure and fundal height, and collect blood and 
urine specimens for storage and future approved testing (Table 3). An untrained operator will 
conduct blind sweep procedures using both a commercial ultrasound machine and an approved 
portable low-cost device. More than one low-cost device may be used for data collection. Next, 
the sonographer will perform full standard biometry procedures and additional protocol-
related ultrasound sweeps using a commercial ultrasound machine. Finally, the trained 
sonographer will perform biometry and blind sweeps with the low-cost device. 
NB: The FAMLI Technology software will not be used to assess gestational age at the time of 
novice or trained sonographer assessment in an effort to avoid biasing sonographer data 
collection during biometry. We will also train sonographers not to ask participants their due 
date or last menstrual period prior to commencing the study scan. 
All participants will be followed through delivery. Study staff will document gestational age, 
vital status, birthweight, and sex at delivery through a combination of medical record review 
Gestational 
Week
GA Range 
(days)
Number 
allocated
Gestational 
Week
GA Range 
(days)
Number 
allocated
140 - 146
98 - 104
28
280 - 286
196 - 202
44
150 - 156
105 - 111
28
290 - 296
203 - 209
44
160 - 166
112 - 118
28
300 - 306
210 - 216
44
170 - 176
119 - 125
28
310 - 316
217 - 223
44
180 - 186
126 - 132
28
320 - 326
224 - 230
44
190 - 196
133 - 139
28
330 - 336
231 - 237
45
200 - 206
140 - 146
29
340 - 346
238 - 244
45
210 - 216
147 - 153
29
350 - 356
245 - 251
45
220 - 226
154 - 160
29
360 - 366
252 - 258
45
230 - 236
161 - 167
29
240 - 246
168 - 174
29
370 - 376
259 - 265
100
250 - 256
175 - 181
29
380 - 386
266 - 272
100
260 - 266
182 - 188
29
390 - 396
273 - 279
100
270 -276
189 - 195
29
400 - 406
280 - 286
100
Primary Evaluation Window
Secondary Evaluation Windows

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
and participant interview as needed. This visit may occur over the phone or in person. Based 
on the timing of delivery, not all participants may complete all three planned study follow-up 
visits.  
TABLE 3: Schedule of Evaluations 
Visit Number 
0.0 
1.0 
2.0 
3.0 
4.0 
Gestational Age (weeks) 
<14 
14-276 
28-366 
37-406 
Delivery 
ADMINISTRATIVE / REGULATORY PROCEDURES 
Informed consent 
• 
 
 
 
 
Confirmation of eligibility 
• 
 
 
 
 
Collection / review of locator info 
• 
• 
• 
• 
• 
Randomization of visit timing 
• 
 
 
 
 
CLINICAL / BEHAVIORAL PROCEDURES 
Obstetrical and medical history 
• 
• 
• 
• 
• 
Anthropometry 
• 
• 
• 
• 
 
Blood pressure measurement 
• 
• 
• 
• 
 
Fundal height measurement 
• 
• 
• 
• 
 
Physical exam 
• 
 
 
 
 
Untrained operator scan  
 
• 
• 
• 
 
Trained sonographer scan 
 
• 
• 
• 
 
LABORATORY PROCEDURES 
Urine storage 
• 
• 
• 
• 
 
Blood storage 
• 
• 
• 
• 
 
6.4 
Retention 
Once a participant is enrolled in the study, the study team will make all reasonable efforts to 
retain her in follow-up to minimize bias associated with loss to follow-up. The study team will 
track retention rates and address any issues related to retention. Strategies will include:  
● Thorough explanation of the study visit schedule and procedures during informed 
consent, and re-emphasis at each study visit. 
● Encouragement of participants to discuss potential study participation with their 
husbands/partners and other influential family members before agreeing to enrol in 
the study.  
● Collection of detailed locator information at screening, and review and updating of 
this information at each study visit. 
● Use of appropriate and timely visit reminder mechanisms (including phone calls and 
text messages). 
● Mobilization of trained outreach workers to complete in-person contact with 
participants at their homes and/or other locations. 
6.5 
Safety monitoring 
At each study visit, study staff will evaluate participants for social harms and adverse events 
(AEs). A social harm will be defined as a non-medical untoward consequence of study 
participation, including: difficulties in personal relationships, stigma, or discrimination from 
family or community. An AE will be defined as any untoward medical occurrence in a study 
participant including any abnormal sign (e.g., abnormal physical exam or laboratory finding), 

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
symptom, or disease, temporally associated with the individual’s participation in the 
research, whether considered related to participation in the research or not. In addition to 
events related to study procedures, which are expected to be very rare, we can expect that 
this population of pregnant women will experience adverse events unrelated to study 
procedures, including adverse obstetrical outcomes, infections, side effects from routine 
medications, hospitalization, and death. 
We expect events to be minimal, but all will be documented, assessed for seriousness / 
severity and relatedness, and carefully monitored. The severity of study-related adverse 
events and social harms will be graded using the National Institute of Health’s Division of AIDS 
Table for Grading the Severity of Adult and Pediatric Adverse Events; this is standard of care 
for all studies conducted at our sites. We will also record information on all serious adverse 
events (SAEs) occurring in maternal participants whether or not they are related to study 
participation, including AEs that: 
1. Result in hospital admission (unless hospitalization is preplanned, i.e., for delivery) or 
prolongation of existing hospitalization,  
2. Are immediately life-threatening,  
3. Cause significant, persistent, or permanent harm or disability, either physical or 
psychological,  
4. Result in death, including fetal demise after 20 weeks of gestation, or 
5. Are congenital anomalies/birth defects. 
Information on adverse events or social harms that are related to the study and all SAEs will 
be documented on study data forms and routinely reported to the Principal Investigator (PI) 
or designee. If the PI, co-investigators, or their designees determine that study-related AEs 
are occurring at an unexpected rate, they will assess the need for and facilitate staff re-
training, protocol amendment, or study cessation. Serious study-related AEs will be reported 
to all regulatory bodies and the study sponsors within 72 hours of site awareness. Other 
events will be reported according to each ethics committees’ individual guidelines.  
6.5.1 Identification and management of clinical complications of pregnancy 
When collecting data or performing physical exams, study staff may identify medical or other 
issues requiring follow-up, treatment, or other clinical care. Similarly, during the course of 
performing ultrasound procedures on study participants, sonographers may identify medical 
issues requiring follow-up, treatment, or other clinical care. All team members will be trained 
on the recognition of adverse events and social harms and what to do if encountered. Safety 
issues requiring follow-up will be brought to the attention of the senior clinical staff, who will 
make referrals if the issue is beyond staff knowledge or skills to be able to adequately address.  
Potential reasons for clinical referral include, but are not limited to:  
- 
Draining of fluid or concern for ruptured membranes 
- 
Vaginal bleeding or concern for threatened abortion or antepartum hemorrhage 
- 
Abnormal vaginal discharge 
- 
Hypertension 
- 
Fetal anomalies 
- 
Abnormalities of fetal growth 
- 
Oligo- or polyhydramnios 
- 
Intrauterine embryonic or fetal demise 

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
- 
Malpresentation at or near term 
- 
Suspected extrauterine pregnancy (ectopic) 
When possible, the participant will be invited to remain in the study. Additional ultrasound 
follow-up procedures as determined by obstetrical management recommendations will be 
provided by study sonographers as requested and free of charge. 
6.6 
Biological specimen collection 
All samples will be obtained from study participants by trained clinical staff according to 
approved standard operating procedures. Biological samples (i.e., blood and urine) will be 
used for untargeted metabolomics and other multi-omic analyses to identify metabolites, 
proteins, transcripts, and host genetic factors associated with fetal maturation, gestational 
age, parturition timing, and adverse outcomes. Each study site will house its own biological 
samples and make plans for off-site redundancy in case of power failures or freezer 
malfunction. Lab testing will be conducted in parallel with the primary analysis and will take 
advantage of the excellent gestational age dating available for all participants and the even 
distribution of sample collection across the gestational age spectrum.  
Use of stored specimens for testing that is not specifically designated in this protocol as 
described above will require additional regulatory approval. Separate written informed 
consent for specimen storage and future use will be specifically obtained during the informed 
consent process.  
6.6.1 Quality control and quality assurance procedures 
Standard Operating Procedures (SOPs) following manufacturer’s protocols and detailing 
technical procedures involved (e.g., sample collection, processing, and storage) will be 
developed and used by the study team. Site coordinators will complete annual recertification. 
The certification process is an opportunity to ensure the highest specimen quality and 
standardize collection techniques. 
6.7 
Data security and management 
Research data will be captured in a dedicated study database. Study data management (e.g., 
data transmission, query resolution, etc.) will follow procedures outlined in study SOPs. Study 
identification numbers will be used on all forms and communications related to the study. A 
separate confidential register will link study identification numbers and participants’ names. 
All data instruments and registers will be securely stored. All study computers and electronic 
devices will be password protected and their access restricted to authorized study personnel. 
Backups of the data will be made routinely. Data may be transmitted electronically to the 
study investigators through secure servers. Study information will not be released without 
written permission of the participant, except when necessary for monitoring by the relevant 
ethical committees or their designees.   
Identifiable data will be maintained for at least ten years after completion of participant 
follow-up. Biological specimens will be incinerated after ten years from collection or as 
dictated by in-country guidelines. Study records will be disposed of following sponsor 
guidelines after being stored in-country for at least three years following the end of study 
follow-up. Paper records will be destroyed (e.g., shredded or incinerated) prior to disposal. 

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
6.8 
Statistical considerations 
6.8.1 Study outcomes 
The primary study outcome is diagnostic accuracy, measured as difference in mean absolute 
error (MAE) of the index test and clinical reference standard in the primary evaluation 
window. MAEs will be calculated as the average of the absolute differences between the GA 
estimated by the index test (i.e., the FAMLI Technology interpreting blind sweep data 
generated by an untrained operator with a low-cost probe) or clinical reference standard (i.e., 
standard fetal biometry performed by a trained sonographer on a commercial ultrasound 
machine) and previously established ground truth GA.  
Secondary study outcomes will be assessed for both the index test and clinical reference 
standard and include (1) MAE in the secondary evaluation windows; (2) root mean squared 
error (RMSE) in all three evaluation windows; and (3) proportion of studies correctly classified 
within 7 days of the ground truth. 
Secondary lab outcomes will be assessed for all participants. Untargeted metabolomics will 
investigate organic acids, organoheterocyclic compounds, lipids and lipid-like molecules, 
benzenoids, organic oxygen compounds, and other minor chemical classes potentially 
associated with gestational age. Other multi-omics markers including proteins, transcripts, 
and host genetic factors may also be analyzed.  
6.8.2 Sample size calculations 
We designed this diagnostic accuracy study to test a non-inferiority hypothesis, such that if 
we observe a 95% confidence interval (CI) to be completely contained in the interval (-c, c) 
then we declare non-inferiority (or equivalence to bound c), with type 1 error 2.5%. We chose 
a sample size such that the statistical power for the non-inferiority hypothesis test is 95%. 
One implication of choosing the above type 1 error and statistical power for this non-
inferiority test is that, if we observe the 95% CI to not be contained in the interval (-c, c), i.e., 
the lower end of the observed 95% CI is smaller than -c or the upper end is larger than c, then 
we may declare superiority, with type 1 error 5%, because the null hypothesis for superiority 
(i.e., a 0-difference) would be excluded from this observed 95% CI. 
We used Monte Carlo simulation to find the sample size that yields a statistical power of at 
least 95% for the non-inferiority test 
with type 1 error of 2.5%, and non-
inferiority bound of c = 2 day error in 
estimated GA. The statistical test is 
based on a difference in mean 
absolute error (MAE), comparing the 
FAMLI 
Technology 
to 
standard 
biometry, with both approaches 
measured in the primary evaluation 
window, which is between 14 0/7 and 
27 6/7 weeks of gestation, inclusive 
(Figure 6). We chose this window for 
our primary evaluation because it 
corresponds to the second trimester 
of pregnancy, when most women 
7: 

 
FAMLI Diagnostic Accuracy 
 
Page 19 of 27 
Protocol v1.3 | 25 May 2023 
first present in low-resource settings (Figure 7). Both the FAMLI Technology-estimated GA 
and standard biometry calculate absolute error with respect to the enrollment “ground truth” 
estimated GA, which is assigned by first trimester ultrasound or, in the case of in vitro 
fertilization, a known embryo age and transfer date.  
We used women enrolled in the ZAPPS cohort with estimated GA < 13 weeks (n = 105) to 
inform the distribution of enrollment GAs, and another ZAPPS estimated GA measured in the 
14 to 27-week window to inform the distribution of primary evaluation EGAs, by the standard 
approach. The enrollment estimated GA by standard biometry (intergrowth formula) was 
74.5 ± 10.5 days. The follow up estimated GA in the primary evaluation window, also obtained 
by standard biometry (Intergrowth formula), was 155.4 ± 6.1 days, and after adjustment 
(subtracting the number of days between enrollment and primary evaluation) was 75.6 ± 12.3 
days. The observed regression function for the adjusted estimated GA during the primary 
evaluation window was -7 + 1.1 (enrollment estimated GA) ± 4 days. Estimates below are 
based on 2000 simulations, which yields simulation error less than 2% for statistical power. 
We assume the GA estimate produced by the FAMLI Technology has properties (i.e., intercept 
and slope) similar to standard biometry, but reduces the random error (τ). In a sensitivity 
analysis, we assumed the FAMLI Technology estimate also removed bias (i.e., intercept 0, 
slope 1), which yields a slight increase in power (results not shown). We do not anticipate that 
the FAMLI Technology will induce bias over the standard approach. We vary the random error 
τ for the FAMLI Technology from τ = 1 to 5, where τ = 4 days is the superiority null hypothesis 
of no difference between approaches. 
Figure 8 shows the statistical power for non-
inferiority as a function of the difference in 
absolute error between approaches (in days) 
and sample size. A horizontal reference line is 
drawn at 95% power. Under our assumptions, 
at a 0 difference in absolute errors between 
approaches,  sample sizes of 200 or more have 
power in excess of 95%.  
Figure 9 shows the statistical power for 
superiority as a function of the difference in 
absolute error between approaches (in days) 
and sample size. At the null hypothesis of 0 
difference, 
the 
statistical 
power 
is 
approximately 5%, regardless of sample size 
(i.e., this is a valid statistical test). At a -0.5-day 
difference in absolute error (i.e., the FAMLI Technology estimated GA reduces random error 
by 1/2 day over standard approach), a sample size of 200 has less than 60% power, while a 
sample size of 400 has 80% power. Figure 10 shows the expected 95% CI as a function of the 
difference in absolute error between approaches (in days) and sample size.  
8: 

 
FAMLI Diagnostic Accuracy 
 
Page 20 of 27 
Protocol v1.3 | 25 May 2023 
 
In conclusion, we settle on a sample size of 400 women for the FAMLI study. Select numeric 
results are also shown in Table 4. 
Table 4: Statistical power for non-inferiority and superiority, by difference in absolute 
error, and sample size * 
  
  
Statistical Power, % 
T 
Days 
Non-inferiority 
Superiority 
  
  
N=200 
N=300 N=400 N=200 N=300 N=400 
1.00 
-2.22 
0 
0 
0 
100 
100 
100 
1.25 
-2.06 
2 
1 
1 
100 
100 
100 
1.50 
-1.89 
9 
10 
12 
100 
100 
100 
1.75 
-1.72 
28 
40 
49 
100 
100 
100 
2.00 
-1.53 
60 
75 
86 
100 
100 
100 
2.25 
-1.35 
83 
94 
98 
100 
100 
100 
2.50 
-1.16 
95 
99 
100 
100 
100 
100 
2.75 
-0.97 
99 
100 
100 
99 
100 
100 
3.00 
-0.77 
100 
100 
100 
89 
98 
100 
3.25 
-0.58 
100 
100 
100 
66 
83 
92 
3.50 
-0.38 
100 
100 
100 
34 
47 
59 
3.75 
-0.20 
100 
100 
100 
12 
15 
18 
4.00 
0.00 
100 
100 
100 
6 
5 
6 
4.25 
0.19 
100 
100 
100 
11 
13 
16 
4.50 
0.38 
100 
100 
100 
28 
39 
50 
4.75 
0.59 
100 
100 
100 
53 
68 
80 
5.00 
0.78 
98 
100 
100 
74 
89 
95 
5.25 
0.98 
91 
98 
100 
89 
97 
99 
5.50 
1.18 
72 
87 
94 
96 
99 
100 
5.75 
1.38 
46 
64 
75 
98 
100 
100 
6.00 
1.58 
26 
33 
42 
100 
100 
100 
* * Monte Carlo simulation error < 2% 
 
 

 
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Protocol v1.3 | 25 May 2023 
6.8.3 Analysis plan 
For each woman, we will determine the estimated GA based on the initial first trimester visit, 
which will be our reference value. Then, for each approach, we will calculate the estimated 
GA and the difference between this estimate and the reference value. In a primary analysis, 
we will compare the mean absolute errors for the different approaches. The approach with 
smaller mean absolute error is preferred. In secondary analyses, we will compare the root 
mean squared errors for the different approaches.  
Because approaches are conducted on all women, comparisons of approaches are naturally 
controlled for all possible time fixed confounding factors. To account for differential loss to 
follow up, if loss to follow up exceeds 9%, and possible resultant selection bias, we will employ 
inverse probability of censoring weights.3 Censoring weights will be estimated using a pooled 
logistic regression model fit by maximum likelihood.4 Continuous covariates will be included 
using restricted quadratic splines.5 
6.9 
Dissemination of findings 
Study findings will be disseminated through appropriate local channels, including academic 
and public health research symposia. One or more publications will also be submitted to a 
peer-reviewed journal. The study participants’ privacy and confidentiality will be strictly 
maintained in all results dissemination or publication activities. 
Sharing of data generated by this project is an essential part of our proposed activities and 
will be carried out in several different ways. The findings of this study will be made available 
through appropriate local channels, including academic and public health research symposia. 
We will make results available to the community of scientists interested in maternal and child 
health to avoid unintentional duplication of research. 
Presentations at national scientific meetings. It is expected that several national meeting 
audiences would be interested in the findings generated by proposal completion. We plan to 
present findings at the annual meeting of the Society for Maternal Fetal Medicine, the annual 
meeting of Pediatric Academic Societies, and the International Conference on Maternal and 
Child Healthcare.  
Access to data. This research will enroll 400 participants, each of whom will attend 3 follow-
up visits in pregnancy (i.e., up to 1,200 research ultrasound scans performed). In addition to 
ultrasound data, the final study dataset will include a limited amount of demographic and 
behavioral data, medical history, pregnancy outcome, and physical exam findings. We will 
make de-identified data and associated documentation available to users only under a data-
sharing agreement that binds the user to: (1) using the data for research purposes; (2) 
securing the data with appropriate technology; (3) destroying or returning the data after 
analyses are completed; (4) making appropriate attribution to data provenance; and (5) 
ensuring the Gates Foundation Global Access requirements are met.  
7.0 
Ethical Considerations 
The protocol, informed consent documents, and any subsequent modifications will be 
reviewed and approved by all relevant ethics committees responsible for oversight of the 
study and maintaining Federal Wide Assurances (FWA) with the Office for Human Research 
Protections (OHRP) approval. For this study, relevant ethics committees include the 

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
University of Zambia Biomedical Research Ethics Committee and the University of North 
Carolina at Chapel Hill Institutional Review Board.  
Participation in this study will be voluntary. All participants involved in prospective collection 
of research data will provide written, informed consent prior to study enrolment. All care and 
procedures will be conducted according to local standards of routine clinical care. All staff 
who have contact with participants will receive training on the protection of human research 
participants prior to conducting any study activities and routinely thereafter. Key staff will 
also complete Good Clinical Practices training. 
7.1 
Informed consent 
Discussions with prospective participants and informed consent procedures will be conducted 
in private to protect patient confidentiality. Where possible, a private room will be used to 
discuss the study and potential participant’s eligibility. If a private room is not available, a 
designated area far enough away from other participants such that they cannot hear the 
conversation will be used. Study staff will obtain written informed consent from all 
participants. The study procedures, risks, and benefits will be discussed, and the responsible 
staff member will answer all questions prior to obtaining consent. The consent forms will be 
translated into local languages and back-translated into English to ensure accurate 
translation. All versions of the consent forms will be approved by the relevant ethics 
committees prior to study initiation. For illiterate participants, a literate impartial witness will 
be present during the entire consent process to ensure that all of the relevant information 
has been provided and the participant voluntarily gives consent.  
Eligible women who do not wish to participate in this study will continue to receive antenatal 
care and treatment according to local clinical standards.   
7.2 
Potential risks to participants  
Investigators will make efforts to minimize risks to participants. This study involves minimal 
risk to participants, no greater than at routine antenatal or clinical examinations. Participants 
will be asked to have additional ultrasound procedures beyond what they would normally 
receive for clinical purposes, but because ultrasound uses sound waves instead of radiation, 
we do not expect this additional exposure to offer any significant risk. Providers have used 
ultrasound for more than 30 years, and they have not found any dangerous risks.  
Physical risks also include the risk of discomfort, bruising or swelling from venipuncture. The 
risks that are associated with venipuncture are infrequent and minimized with the use of 
proper technique. Such risks include (1) bleeding, (2) bruising, or (3) rarely infection at the 
site of needle insertion. Individuals may also rarely become faint, in which case symptoms 
abate after several minutes in a recumbent position. Blood volumes for the study have been 
calculated to ensure safety and participants will be reassured that such feelings are transient. 
Participation in clinical research includes the risks of loss of confidentiality and discomfort 
with the personal nature of questions, particularly when discussing HIV infection or sexual 
behaviors. At each step in the study, we will protect participant privacy and confidentiality to 
reduce these risks (e.g., consenting participants in a private setting, not including names on 
case report forms, etc.). Although investigators make every effort to protect participant 
privacy and confidentiality, it is possible that participant involvement in the study could 
become known to others, and that social harms may result. 

 
FAMLI Diagnostic Accuracy 
 
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Protocol v1.3 | 25 May 2023 
The confidentiality of all study records will be safeguarded to the extent legally possible. To 
maintain participant confidentiality, all laboratory specimens, reports, study data and 
administrative forms will be identified by a coded number only. All databases will be secured 
with password-protected access systems, and computer entries will be identified by coded 
number only. Forms, lists, logbooks, appointment books, and any other listings or data forms 
that link participant ID numbers to other identifying information will be stored in a separate, 
locked cabinet. All data analysis will be performed using datasets which have only the study 
number as a unique identifier. Clinical information with individual identifiers will not be 
released without the written permission of the participant. We expect these procedures to 
adequately protect participant confidentiality.  
7.3 
Potential benefits to participants and others  
Individual participants may benefit from clinical monitoring through routine ultrasounds and 
health assessments, with referrals provided as needed. They may also receive no direct 
benefit. Women may appreciate the opportunity to see their fetus during pregnancy and will 
be provided with printed or electronic copies of ultrasound images.   
Knowledge generated from this study has the potential to contribute to the development of 
widely deployable ultrasound technologies that can assess gestational age and other 
important obstetric conditions while requiring minimal user expertise, which has the 
potential to improve maternal and infant outcomes in low-resource settings worldwide.  
In summary, risks to participants in this study are minimal and do not differ significantly from 
the risks inherent in the local standard of care for pregnant women. These risks are 
reasonable in relation to the anticipated benefits of the project.  
7.4 
Inclusion of children, sub-populations, and vulnerable populations  
This study focuses specifically on pregnant women and, as such, they must be included in our 
study population. Prisoners will be excluded as they receive care at separate facilities. 
8.0 
Timeline 
We have allocated 3 months to start-up activities and 21 months to study implementation. 
Month Q1 
Q2 
Q3 
Q4 
Q5 
Q6 
Q7 
Q8 
Protocol and CRF development   
  
  
 
  
  
 
 
Ethical submission and review   
  
  
 
  
  
 
 
Site preparation  
 
 
 
 
 
 
 
Site training  
 
 
 
 
 
 
 
Enrollment  
  
  
  
  
  
 
 
Data entry and management  
  
  
  
  
  
 
 
Follow-up  
 
 
 
 
 
 
 
Statistical analysis  
  
  
 
  
  
 
 
Publication and dissemination  
  
  
 
  
  
 
 
9.0 
Budget – Zambia field costs 
The following costs are projected over the two-year study period.   
ITEM 
ZMW TOTAL 
USD TOTAL 
Personnel 
3,750,000 
$250,000 
Equipment 
750,000 
$50,000 

 
FAMLI Diagnostic Accuracy 
 
Page 24 of 27 
Protocol v1.3 | 25 May 2023 
Supplies & materials 
457,500 
$30,500 
Participant reimbursement 
180,000 
$12,000 
Other – communication, data, translation, regulatory, 
community outreach, shipping, and training costs  
937,500 
$62,500 
TOTAL 
6.075,000 
$405,000 
 
 

 
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Protocol v1.3 | 25 May 2023 
10.0 
Reference List 
1. 
Marsh-Feiley G, Eadie L, Wilson P. Telesonography in emergency medicine: A 
systematic review. PloS one. 2018;13(5):e0194840. 
2. 
Becker DM, Tafoya CA, Becker SL, Kruger GH, Tafoya MJ, Becker TK. The use of 
portable ultrasound devices in low- and middle-income countries: a systematic 
review of the literature. Trop Med Int Health. 2016;21(3):294-311. 
3. 
Carin L, Pencina MJ. On Deep Learning for Medical Image Analysis. In: Livingston EH, 
Lewis RJ, eds. JAMA Guide to Statistics and Methods. New York, NY: McGraw-Hill 
Education; 2019. 
4. 
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. 
Nature medicine. 2019;25(1):24-29. 
5. 
Kassebaum NJ, Bertozzi-Villa A, Coggeshall MS, et al. Global, regional, and national 
levels and causes of maternal mortality during 1990-2013: a systematic analysis for 
the Global Burden of Disease Study 2013. Lancet. 2014;384(9947):980-1004. 
6. 
Alkema L, Chou D, Hogan D, et al. Global, regional, and national levels and trends in 
maternal mortality between 1990 and 2015, with scenario-based projections to 
2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency 
Group. Lancet. 2016;387(10017):462-474. 
7. 
Liu L, Oza S, Hogan D, et al. Global, regional, and national causes of under-5 mortality 
in 2000-15: an updated systematic analysis with implications for the Sustainable 
Development Goals. Lancet. 2016;388(10063):3027-3035. 
8. 
Lawn JE, Blencowe H, Waiswa P, et al. Stillbirths: rates, risk factors, and acceleration 
towards 2030. Lancet. 2016;387(10018):587-603. 
9. 
Yadav H, Shah D, Sayed S, Horton S, Schroeder LF. Availability of essential diagnostics 
in ten low-income and middle-income countries: results from national health facility 
surveys. The Lancet Global health. 2021. 
10. 
Stulberg DB, Cain LR, Dahlquist I, Lauderdale DS. Ectopic pregnancy rates and racial 
disparities in the Medicaid population, 2004-2008. Fertil Steril. 2014;102(6):1671-
1676. 
11. 
Smits J, Monden C. Twinning across the Developing World. PloS one. 
2011;6(9):e25239. 
12. 
Cresswell JA, Ronsmans C, Calvert C, Filippi V. Prevalence of placenta praevia by 
world region: a systematic review and meta-analysis. Trop Med Int Health. 
2013;18(6):712-724. 
13. 
Obstetric care consensus no. 1: safe prevention of the primary cesarean delivery. 
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