Small for Gestational Age InfantNCT05059093
Developing and Testing AI Models for Fetal Biometry and Amniotic Volume Assessment in Fetal Ultrasound Scans.
An observational study of Small for Gestational Age Infant and Fetal Growth Restriction, run by Deepecho at 6 sites in Morocco.
Trial summary
Routine fetal ultrasound scan during the second trimester of the pregnancy is a low-cost, noninvasive screening modality that has been proven to lower fetal mortality by up to 20%. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Small for Gestational Age Infant, Fetal Growth Restriction, Oligohydramnios
- What is being tested?
- Classification and segmentation deep learning models
- How many people
- 122 participants
- Where
- 6 sitesMorocco
- Study dates
- 25 Oct 2021to 1 Apr 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: Pregnant women from 18 years onwards scheduled for a routine fetal ultrasound scan
Who may be able to join
- Single or multiple viable pregnancies with a gestational age of 14 weeks or more as dated on a first trimester US scan with the crown-rump length (CRL) measurement or grossly estimated from the last menstrual period (LMP).
- Routine programmed US scan.
- Patient's consent is obtained.
- Patient over 18 years old.
Who cannot join
- Emergency indication for the fetal ultrasound
- Major morphological malformations that do not allow proper measurement of the cranium, abdominal or lower limb, for example, anencephaly, omphalocele, lower limb phocomelia.
- Fetal death.
Trial locations and contacts
6 sites in 1 country.
6 sites in 1 country
Morocco6 sites
Centre de Radiologie Abou Madi
Casablanca, 20100
Centre Hospitalier Cheikh Khalifa
Casablanca, 20100
Centre Hospitalier Universitaire Ibn Rochd
Casablanca, 20100
Mohamed VI University International Hospital
Casablanca, 27182
Centre Hospitalier Universitaire Hassan II Fes
Fes
Centre Hospitalier Universitaire Mohammed VI Oujda
Oujda
About this study
Official title: Developing and Testing Deep Learning Models for Fetal Biometry and Amniotic Volume Assessment in Routine Fetal Ultrasound Scans
Routine fetal ultrasound scan during the second trimester of the pregnancy is a low-cost, noninvasive screening modality that has been proven to lower fetal mortality by up to 20%. One of the critical elements of this exam is the measurement of fetal biometric parameters, which are the head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC), and femur length (FL) measured on biometry standard planes. Those standard planes are taken according to quality standards first described by Salomon et al. and used as the guidelines of the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG). The biometric parameters extracted from them are essential to diagnose fetal growth restriction (FGR), the world's first cause of perinatal fetal mortality.
Such measurements and image quality assessment are time-consuming tasks that are prone to inter and intraobserver variability depending on the level of skill of the sonographer or the physician performing the exam.
Read more
Amniotic fluid (AF) volume assessment is also an essential step in routine screening scans allowing the diagnosis of oligo or hydramnios, both associated with increased fetal mortality rates.
The AF is measured by two main "semi-quantitative" techniques: Amniotic Fluid Index (AFI) and the single deepest pocket (SDP). The latter is more specific as it lowers the overdiagnosis of oligo-amnios without any impact on mortality or morbidity and is easier to perform for the sonographer (only one measurement versus four in the case of the AFI technique). However, AF assessment remains a time-consuming and poorly reproducible task.
Attempts to automate such biometric measurements and AF volume assessment have been made using Artificial Intelligence (AI) and deep learning (DL) tools. Studies showed excellent results "in silico," reaching up to 98 %, 95%, 93 % dice score coefficients for HC, AC, and FL measurements and 89 % DSC for AFI measurements. However, they were all conducted retrospectively without validation on prospectively acquired images.
Reviews and experts have stressed the need for quality peer-reviewed prospective studies to assess AI tools' performance with real-world data. Their performance is expected to be worse and to reflect better their use in the clinical workflow.
This study aims to develop DL models to automate HC, BPD, AC, and FL measurements and AF volume assessment from retrospectively acquired data and test their performances to those of clinicians and experts on prospective real-world fetal US scans.
Read the detailed description
The DL models will be trained, validated, and tested on the retrospectively acquired data first. This data will consist of fetal US images gathered in the participating medical centers after patient-level anonymization. The ground truth for the models will consist of annotations made by radiologists and obstetricians for classification and segmentation purposes. The DL models will be trained to perform the following tasks:
- Detection of the following standard planes as described in the ISUOG guidelines: transthalamic, transventricular, transcerebellar, abdominal, and femoral planes on video loops.
- Image quality scoring according to the ISUOG guidelines of the transthalamic, abdominal and femoral planes.
- Fetal cranium, abdomen, and femur segmentation to measure HC, BPD AC, and FL.
- Detection of AF pockets.
- Segmentation of AF pockets and extraction of pockets depth in order to evaluate the SDP measurement
Physicians will be asked to save additional images and video loops additional to their routine screening in the prospective examinations:
- Eight images: transthalamic, abdominal, and femoral standard planes with and without calipers, SDP with and without calipers.
- Four video loops up to five seconds each:
- A cephalic loop encompassing the transcerebellar, transthalamic, and transventricular planes.
- An abdominal loop going from the four-chamber view of the heart to a cross-section of the kidneys and back.
- A femoral loop with the probe parallel to the sagittal axis of the femur sweeping from side to side.
- A whole amniotic cavity loop, with the probe perpendicular to the ground applying as little pressure as possible on the patient's abdomen, sweeping from the uterine fundus to the cervix, once or twice depending on the volume of the amniotic cavity.
The clinicians performing the exam in "real-time"(RT clinicians), the panel of experts, and the DL models will review the prospective examinations.
The SDP measurement extracted by the AF pocket detection and segmentation models will be directly compared to the value measured by the RT clinicians.
Then, the image quality of planes selected by the RT clinicians and the model will be scored by the panel of experts.
The segmentation task will be evaluated in a tripartite fashion: the model, the RT clinicians, and the panel will all segment the same images.
To assess inter-observer agreement, 10% of the images will be randomly selected and reviewed by two independent reviewers from the panel.
- Conditions
- Small for Gestational Age Infant, Fetal Growth Restriction, Oligohydramnios, Polyhydramnios
- Being tested
- Classification and segmentation deep learning models
- Keywords
- Deep Learning, Artificial Intelligence, Fetal Ultrasound, Fetal Biometry, Amniotic Fluid Volume Measurement
- Medical subject headings
- Fetal Growth Retardation, Oligohydramnios, Polyhydramnios
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Classification and segmentation deep learning modelsDiagnostic test
Models that will be trained on retrospectively acquired data and run on the prospectively acquired data to extract biometric parameters and amniotic volume estimation.
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall accuracy for the biometric parameters measurement and amniotic fluid volume assessment
Mean Absolute Error between the model's HC, BPD, AC, FL, and SDP measurements (in mm), the RT clinician's, and the panel's
Measured: up to 20 weeks
Secondary outcomes (3)
Image quality
Overall model's and RT clinician's image quality score assessed by the panel following the ISUOG standards on fetal ultrasound assessment of Biometry and Growth
Measured: Up to 20 weeks
Small-for-Gestational-Age fetus detection accuracy, sensitivity and specificity
Overall models' diagnostic accuracy, sensitivity, and specificity at detecting Small-for-Gestational-Age fetuses compared to RT clinicians
Measured: Up to 20 weeks
Oligohydramnios and polyhydramnios detection accuracy, sensitivity, and specificity
Overall models' diagnostic accuracy, sensitivity, and specificity at detecting oligohydramnios and polyhydramnios compared to RT clinicians
Measured: Up to 20 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Sep 2021
- Study start25 Oct 2021
- Primary completion1 Apr 2022
- Study completion1 Apr 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for Small for Gestational Age InfantStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 122 participants (actual)
Sponsor and investigators
- Lead sponsor
- Deepecho
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Centre Hospitalier Universitaire Ibn Rochd, Hassan II University, Mohammed VI University Hospital, Mohammed V Souissi University
- Responsible party
- Sponsor
- Investigators
- Saad Slimani, M.D., Principal investigator, Centre Hospitalier Universitaire Ibn Rochd de Casablanca
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05059093
- Sponsor's protocol ID
- U1111-1268-5186
- Status last verified
- Jul 2022
- First submitted
- 7 Sep 2021
- Last updated
- 27 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Investigators plan to communicate findings primarily through original research papers and through participation in scientific meetings. In addition, the investigators will communicate with the general public through the media. The inclusion of co-authors will follow the ICMJE recommendations for scientific publications. Access to the study data might be granted to academic researchers but not to the general public.
Publications
Grytten J, Skau I, Sorensen R, Eskild A. Does the Use of Diagnostic Technology Reduce Fetal Mortality? Health Serv Res. 2018 Dec;53(6):4437-4459. doi: 10.1111/1475-6773.12721. Epub 2018 Jan 19.
PubMed 29349772Salomon LJ, Alfirevic Z, Berghella V, Bilardo C, Hernandez-Andrade E, Johnsen SL, Kalache K, Leung KY, Malinger G, Munoz H, Prefumo F, Toi A, Lee W; ISUOG Clinical Standards Committee. Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol. 2011 Jan;37(1):116-26. doi: 10.1002/uog.8831. No abstract available.
PubMed 20842655Gaudineau A. [Prevalence, risk factors, maternal and fetal morbidity and mortality of intrauterine growth restriction and small-for-gestational age]. J Gynecol Obstet Biol Reprod (Paris). 2013 Dec;42(8):895-910. doi: 10.1016/j.jgyn.2013.09.013. Epub 2013 Nov 9. French.
PubMed 24216305Sarris I, Ioannou C, Chamberlain P, Ohuma E, Roseman F, Hoch L, Altman DG, Papageorghiou AT; International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). Intra- and interobserver variability in fetal ultrasound measurements. Ultrasound Obstet Gynecol. 2012 Mar;39(3):266-73. doi: 10.1002/uog.10082.
PubMed 22535628Tashfeen K, Hamdi IM. Polyhydramnios as a predictor of adverse pregnancy outcomes. Sultan Qaboos Univ Med J. 2013 Feb;13(1):57-62. doi: 10.12816/0003196. Epub 2013 Feb 27.
PubMed 23573383Morris RK, Meller CH, Tamblyn J, Malin GM, Riley RD, Kilby MD, Robson SC, Khan KS. Association and prediction of amniotic fluid measurements for adverse pregnancy outcome: systematic review and meta-analysis. BJOG. 2014 May;121(6):686-99. doi: 10.1111/1471-0528.12589. Epub 2014 Feb 7.
PubMed 24738894Kehl S, Schelkle A, Thomas A, Puhl A, Meqdad K, Tuschy B, Berlit S, Weiss C, Bayer C, Heimrich J, Dammer U, Raabe E, Winkler M, Faschingbauer F, Beckmann MW, Sutterlin M. Single deepest vertical pocket or amniotic fluid index as evaluation test for predicting adverse pregnancy outcome (SAFE trial): a multicenter, open-label, randomized controlled trial. Ultrasound Obstet Gynecol. 2016 Jun;47(6):674-9. doi: 10.1002/uog.14924.
PubMed 26094600Sande JA, Ioannou C, Sarris I, Ohuma EO, Papageorghiou AT. Reproducibility of measuring amniotic fluid index and single deepest vertical pool throughout gestation. Prenat Diagn. 2015 May;35(5):434-9. doi: 10.1002/pd.4504. Epub 2015 Mar 28.
PubMed 25297394Li Y, Zhang Z, Dai C, Dong Q, Badrigilan S. Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis. Comput Biol Med. 2020 Aug;123:103898. doi: 10.1016/j.compbiomed.2020.103898. Epub 2020 Jul 14.
PubMed 32768045Dhar R, Falcone GJ, Chen Y, Hamzehloo A, Kirsch EP, Noche RB, Roth K, Acosta J, Ruiz A, Phuah CL, Woo D, Gill TM, Sheth KN, Lee JM. Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage. Stroke. 2020 Feb;51(2):648-651. doi: 10.1161/STROKEAHA.119.027657. Epub 2019 Dec 6.
PubMed 31805845Sekhar A, Biswas S, Hazra R, Sunaniya AK, Mukherjee A, Yang L. Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System. IEEE J Biomed Health Inform. 2022 Mar;26(3):983-991. doi: 10.1109/JBHI.2021.3100758. Epub 2022 Mar 7.
PubMed 34324425Kim HP, Lee SM, Kwon JY, Park Y, Kim KC, Seo JK. Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiol Meas. 2019 Jul 1;40(6):065009. doi: 10.1088/1361-6579/ab21ac.
PubMed 31091515Sobhaninia Z, Rafiei S, Emami A, Karimi N, Najarian K, Samavi S, Reza Soroushmehr SM. Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:6545-6548. doi: 10.1109/EMBC.2019.8856981.
PubMed 31947341Cho HC, Sun S, Min Hyun C, Kwon JY, Kim B, Park Y, Seo JK. Automated ultrasound assessment of amniotic fluid index using deep learning. Med Image Anal. 2021 Apr;69:101951. doi: 10.1016/j.media.2020.101951. Epub 2021 Jan 7.
PubMed 33515982Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019 Oct 29;17(1):195. doi: 10.1186/s12916-019-1426-2.
PubMed 31665002
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing and Testing Deep Learning Models for Fetal Biometry and Amniotic Volume Assessment in Routine Fetal Ultrasound Scans
- ClinicalTrials.gov ID
- NCT05059093
- Acronym / sponsor ID
- U1111-1268-5186
- Conditions
- Small for Gestational Age Infant, Fetal Growth Restriction, Oligohydramnios, Polyhydramnios
- Keywords
- Deep Learning, Artificial Intelligence, Fetal Ultrasound, Fetal Biometry, Amniotic Fluid Volume Measurement
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 122 participants (actual)
- Lead sponsor
- DeepechoIndustry (pharma, biotech, devices)
- Collaborators
- Centre Hospitalier Universitaire Ibn Rochd, Hassan II University, Mohammed VI University Hospital, Mohammed V Souissi University
- Study start
- 25 Oct 2021 (actual)
- Primary completion
- 1 Apr 2022 (actual)
- Study completion
- 1 Apr 2022 (actual)
- First posted
- 28 Sep 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Routine fetal ultrasound scan during the second trimester of the pregnancy is a low-cost, noninvasive screening modality that has been proven to lower fetal mortality by up to 20%. One of the critical elements of this exam is the measurement of fetal biometric parameters, which are the head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC), and femur length (FL) measured on biometry standard planes. Those standard planes are taken according to quality standards first described by Salomon et al. and used as the guidelines of the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG). The biometric parameters extracted from them are essential to diagnose fetal growth restriction (FGR), the world's first cause of perinatal fetal mortality.
Such measurements and image quality assessment are time-consuming tasks that are prone to inter and intraobserver variability depending on the level of skill of the sonographer or the physician performing the exam.
Amniotic fluid (AF) volume assessment is also an essential step in routine screening scans allowing the diagnosis of oligo or hydramnios, both associated with increased fetal mortality rates.
The AF is measured by two main "semi-quantitative" techniques: Amniotic Fluid Index (AFI) and the single deepest pocket (SDP). The latter is more specific as it lowers the overdiagnosis of oligo-amnios without any impact on mortality or morbidity and is easier to perform for the sonographer (only one measurement versus four in the case of the AFI technique). However, AF assessment remains a time-consuming and poorly reproducible task.
Attempts to automate such biometric measurements and AF volume assessment have been made using Artificial Intelligence (AI) and deep learning (DL) tools. Studies showed excellent results "in silico," reaching up to 98 %, 95%, 93 % dice score coefficients for HC, AC, and FL measurements and 89 % DSC for AFI measurements. However, they were all conducted retrospectively without validation on prospectively acquired images.
Reviews and experts have stressed the need for quality peer-reviewed prospective studies to assess AI tools' performance with real-world data. Their performance is expected to be worse and to reflect better their use in the clinical workflow.
This study aims to develop DL models to automate HC, BPD, AC, and FL measurements and AF volume assessment from retrospectively acquired data and test their performances to those of clinicians and experts on prospective real-world fetal US scans.
Detailed description
The DL models will be trained, validated, and tested on the retrospectively acquired data first. This data will consist of fetal US images gathered in the participating medical centers after patient-level anonymization. The ground truth for the models will consist of annotations made by radiologists and obstetricians for classification and segmentation purposes. The DL models will be trained to perform the following tasks:
- Detection of the following standard planes as described in the ISUOG guidelines: transthalamic, transventricular, transcerebellar, abdominal, and femoral planes on video loops.
- Image quality scoring according to the ISUOG guidelines of the transthalamic, abdominal and femoral planes.
- Fetal cranium, abdomen, and femur segmentation to measure HC, BPD AC, and FL.
- Detection of AF pockets.
- Segmentation of AF pockets and extraction of pockets depth in order to evaluate the SDP measurement
Physicians will be asked to save additional images and video loops additional to their routine screening in the prospective examinations:
- Eight images: transthalamic, abdominal, and femoral standard planes with and without calipers, SDP with and without calipers.
- Four video loops up to five seconds each:
- A cephalic loop encompassing the transcerebellar, transthalamic, and transventricular planes.
- An abdominal loop going from the four-chamber view of the heart to a cross-section of the kidneys and back.
- A femoral loop with the probe parallel to the sagittal axis of the femur sweeping from side to side.
- A whole amniotic cavity loop, with the probe perpendicular to the ground applying as little pressure as possible on the patient's abdomen, sweeping from the uterine fundus to the cervix, once or twice depending on the volume of the amniotic cavity.
The clinicians performing the exam in "real-time"(RT clinicians), the panel of experts, and the DL models will review the prospective examinations.
The SDP measurement extracted by the AF pocket detection and segmentation models will be directly compared to the value measured by the RT clinicians.
Then, the image quality of planes selected by the RT clinicians and the model will be scored by the panel of experts.
The segmentation task will be evaluated in a tripartite fashion: the model, the RT clinicians, and the panel will all segment the same images.
To assess inter-observer agreement, 10% of the images will be randomly selected and reviewed by two independent reviewers from the panel.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Pregnant women from 18 years onwards scheduled for a routine fetal ultrasound scan
Key inclusion criteria (4 listed)
- Single or multiple viable pregnancies with a gestational age of 14 weeks or more as dated on a first trimester US scan with the crown-rump length (CRL) measurement or grossly estimated from the last menstrual period (LMP).
- Routine programmed US scan.
- Patient's consent is obtained.
- Patient over 18 years old.
Key exclusion criteria (3 listed)
- Emergency indication for the fetal ultrasound
- Major morphological malformations that do not allow proper measurement of the cranium, abdominal or lower limb, for example, anencephaly, omphalocele, lower limb phocomelia.
- Fetal death.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Classification and segmentation deep learning modelsModels that will be trained on retrospectively acquired data and run on the prospectively acquired data to extract biometric parameters and amniotic volume estimation. | Diagnostic test | – |
Primary outcome
Overall accuracy for the biometric parameters measurement and amniotic fluid volume assessment
Time frame: up to 20 weeks
Mean Absolute Error between the model's HC, BPD, AC, FL, and SDP measurements (in mm), the RT clinician's, and the panel's
3 secondary outcomes
Image quality
Time frame: Up to 20 weeks
Small-for-Gestational-Age fetus detection accuracy, sensitivity and specificity
Time frame: Up to 20 weeks
Oligohydramnios and polyhydramnios detection accuracy, sensitivity, and specificity
Time frame: Up to 20 weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jul 2022.
- Background: PubMed 29349772Grytten J, Skau I, Sorensen R, Eskild A. Does the Use of Diagnostic Technology Reduce Fetal Mortality? Health Serv Res. 2018 Dec;53(6):4437-4459. doi: 10.1111/1475-6773.12721. Epub 2018 Jan 19.
- Background: PubMed 20842655Salomon LJ, Alfirevic Z, Berghella V, Bilardo C, Hernandez-Andrade E, Johnsen SL, Kalache K, Leung KY, Malinger G, Munoz H, Prefumo F, Toi A, Lee W; ISUOG Clinical Standards Committee. Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol. 2011 Jan;37(1):116-26. doi: 10.1002/uog.8831. No abstract available.
- Background: PubMed 24216305Gaudineau A. [Prevalence, risk factors, maternal and fetal morbidity and mortality of intrauterine growth restriction and small-for-gestational age]. J Gynecol Obstet Biol Reprod (Paris). 2013 Dec;42(8):895-910. doi: 10.1016/j.jgyn.2013.09.013. Epub 2013 Nov 9. French.
- Background: PubMed 22535628Sarris I, Ioannou C, Chamberlain P, Ohuma E, Roseman F, Hoch L, Altman DG, Papageorghiou AT; International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). Intra- and interobserver variability in fetal ultrasound measurements. Ultrasound Obstet Gynecol. 2012 Mar;39(3):266-73. doi: 10.1002/uog.10082.
- Background: PubMed 23573383Tashfeen K, Hamdi IM. Polyhydramnios as a predictor of adverse pregnancy outcomes. Sultan Qaboos Univ Med J. 2013 Feb;13(1):57-62. doi: 10.12816/0003196. Epub 2013 Feb 27.
- Background: PubMed 24738894Morris RK, Meller CH, Tamblyn J, Malin GM, Riley RD, Kilby MD, Robson SC, Khan KS. Association and prediction of amniotic fluid measurements for adverse pregnancy outcome: systematic review and meta-analysis. BJOG. 2014 May;121(6):686-99. doi: 10.1111/1471-0528.12589. Epub 2014 Feb 7.
- Background: PubMed 26094600Kehl S, Schelkle A, Thomas A, Puhl A, Meqdad K, Tuschy B, Berlit S, Weiss C, Bayer C, Heimrich J, Dammer U, Raabe E, Winkler M, Faschingbauer F, Beckmann MW, Sutterlin M. Single deepest vertical pocket or amniotic fluid index as evaluation test for predicting adverse pregnancy outcome (SAFE trial): a multicenter, open-label, randomized controlled trial. Ultrasound Obstet Gynecol. 2016 Jun;47(6):674-9. doi: 10.1002/uog.14924.
- Background: PubMed 25297394Sande JA, Ioannou C, Sarris I, Ohuma EO, Papageorghiou AT. Reproducibility of measuring amniotic fluid index and single deepest vertical pool throughout gestation. Prenat Diagn. 2015 May;35(5):434-9. doi: 10.1002/pd.4504. Epub 2015 Mar 28.
- Background: PubMed 32768045Li Y, Zhang Z, Dai C, Dong Q, Badrigilan S. Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis. Comput Biol Med. 2020 Aug;123:103898. doi: 10.1016/j.compbiomed.2020.103898. Epub 2020 Jul 14.
- Background: PubMed 31805845Dhar R, Falcone GJ, Chen Y, Hamzehloo A, Kirsch EP, Noche RB, Roth K, Acosta J, Ruiz A, Phuah CL, Woo D, Gill TM, Sheth KN, Lee JM. Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage. Stroke. 2020 Feb;51(2):648-651. doi: 10.1161/STROKEAHA.119.027657. Epub 2019 Dec 6.
- Background: PubMed 34324425Sekhar A, Biswas S, Hazra R, Sunaniya AK, Mukherjee A, Yang L. Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System. IEEE J Biomed Health Inform. 2022 Mar;26(3):983-991. doi: 10.1109/JBHI.2021.3100758. Epub 2022 Mar 7.
- Background: PubMed 31091515Kim HP, Lee SM, Kwon JY, Park Y, Kim KC, Seo JK. Automatic evaluation of fetal head biometry from ultrasound images using machine learning. Physiol Meas. 2019 Jul 1;40(6):065009. doi: 10.1088/1361-6579/ab21ac.
- Ultrasound assessment of fetal biometry and growth ISUOG GuidelinesLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. Investigators plan to communicate findings primarily through original research papers and through participation in scientific meetings. In addition, the investigators will communicate with the general public through the media. The inclusion of co-authors will follow the ICMJE recommendations for scientific publications. Access to the study data might be granted to academic researchers but not to the general public.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Saad Slimani, M.D. principal investigator
Centre Hospitalier Universitaire Ibn Rochd de Casablanca
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Small for Gestational Age Infant and Fetal Growth Restriction, run by Deepecho at 6 sites in Morocco.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in Morocco. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall accuracy for the biometric parameters measurement and amniotic fluid volume assessment (time frame: up to 20 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05059093, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jul 2022.