Congenital AbnormalitiesPARADISENCT05738954
Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning
An observational study of Congenital Abnormalities, run by University of Craiova at 1 site in Romania.
Trial summary
Congenital anomalies (CA) are the most encountered cause of fetal death, infant mortality and morbidity.7.9 million infants are born with CA yearly. Early detection of CA facilitates life-saving treatments and stops the progression of disabilities. CA can be diagnosed prenatally through Morphology Scan (MS). Read more about this study
- Who is it for?
- Ages 18 to 50 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Congenital Abnormalities
- What is being tested?
- Ultrasound
- How many people
- 4,000 participants
- Where
- 1 siteRomania
- Study dates
- 4 May 2022to 31 Dec 2024
- 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 to 50 years
- Women only
- Healthy volunteers accepted
- Adult
Study population: The pregnant women that have their second trimester morphology scan scheduled. They are included in the study consecutively.
Who may be able to join
- Second trimester pregnant women
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
Romania1 site, 1 recruiting
- Recruiting
University Emergency County Hospital
Craiova, Dolj, 200643
Dominic G Iliescu, Assoc.Prof., 723888773 ext. +40, dominic.iliescu@yahoo.com
About this study
Congenital anomalies (CA) are the most encountered cause of fetal death, infant mortality and morbidity.7.9 million infants are born with CA yearly. Early detection of CA facilitates life-saving treatments and stops the progression of disabilities. CA can be diagnosed prenatally through Morphology Scan (MS). Discrepancies between pre and postnatal diagnosis of CA reach 29%. A correct interpretation of MS allows a detailed discussion regarding the prognosis with parents. The central feature of PARADISE is the development of a specialized intelligent system that embeds a committee of Deep Learning and Statistical Learning methods, which work together in a competitive/collaborative way to increase the performance of MS examinations by signaling CA. Using preclinical testing and clinical validation, the main goal will be the direct implementation into clinical practice. This multi-disciplinary project offers a unique integration of approaches, competences, breakthroughs in key applications in human, psychological, technological, and economical interest such as the 'smarter' healthcare system, opening new fields of research. PARADISE creates an environment that contributes significantly to the healthcare system, medical and pharma industries, scientific community, economy and ultimately to each individual. Its outcome will increase impact on the management of CA by enabling the establishment of detailed plans before birth, which will decrease morbidity and mortality in infants.
Read the detailed description
Probe guidance: The IS guides the sonographer's probe for better acquisition of the fetal biometric plane - Basic scanning to be performed by non-expert(> 90% accuracy (AC)) Fetal biometric plane finder: The fetal planes are automatically detected, measured and stored - Insurance that all anatomical parts are checked (100% AC) Anomaly detection: unusual findings are signaled - Assistance in decision making (>90% AC)
- Conditions
- Congenital Abnormalities
- Being tested
- Ultrasound
- Keywords
- ultrasound, fetal morphology, view planes, deep learning, learning curves
- Medical subject headings
- Congenital Abnormalities
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
UltrasoundOther
Collect patient data, anonymize and label it. Written informed consent or verbal recorded consent (if the participant lacks the ability to write or sign) will be obtained before performing the MS. In the unlikely event that some of the participants will withdraw their consent after the ultrasound has been performed, the data collected will not be used in the project. Data for publications and dataset will be previously made anonymous following standard practices. The participants will sign a GDPR form.
The DL/SL algorithms will work in a competitive/collaborative way. Following the 'no-free-lunch' theorem, we shall use the competitive phase to establish the most suitable DL/SL technique for the identification and anomaly detection of each organ, and the collaborative phase to make all the algorithms work together in providing a 'second' opinion.
| Study group | Role | What they receive |
|---|---|---|
| Second trimester | Second trimester fetal morphology Collect patient data, anonymize and label it. Written informed consent or verbal recorded consent (if the participant lacks the ability to write or sign) will be obtained before performing the MS. In the unlikely event that some of the participants will withdraw their consent after the ultrasound has been performed, the data collected will not be used in the project. Data for publications and dataset will be previously made anonymous following standard practices. The participants will sign a GDPR form. Receives: Other: Ultrasound |
What is being measured
The primary outcome is the main question the study is designed to answer.
Signal congenital anomalies
Number of congetinal anomalies found in a fetus at the second trimester morphology scan
Measured: 32 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Feb 2023
- Study start4 May 2022
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Congenital AbnormalitiesStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Craiova
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Smaranda Belciug, Assoc. Prof., Principal investigator, University of Craiova
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05738954
- Sponsor's protocol ID
- PARADISE
- Acronym
- PARADISE
- Status last verified
- Sep 2023
- First submitted
- 1 Feb 2023
- Last updated
- 13 Sep 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Element 1: Data Type Primary and secondary data from RGB 2D ultrasound images (4000 images) The data can be used by researchers to further improve the diagnostic of congenital anomalies. Element 2: Related Tools, Software and/or Code: There will be no specific tools for accessing data. Element 4: Data Preservation, Access, and Associated Timelines A. Repository where scientific data and metadata will be archived: www.zenodo.org When and how long the scientific data will be made available: December 2024-December 2034 Element 5: Access, Distribution, or Reuse Considerations Informed consent, anonymized data, privacy constraints and applicable ethical norms, national laws, privacy policies Element 6: Oversight of Data Management and Sharing: Renato Constantin Ivanescu- anonymizing the data and collecting it Dominic Iliescu, Rodica Nagy, Anca Ofiteru, Cristina Comanescu - gathering data Smaranda Belciug - overall supervision role for data management
Publications
Belciug S. Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing. Comput Biol Med. 2022 Jul;146:105623. doi: 10.1016/j.compbiomed.2022.105623. Epub 2022 May 17.
PubMed 35751202Belciug S, Ivanescu RC, Popa SD, Iliescu DG. Doctor/Data Scientist/Artificial Intelligence Communication Model. Case Study. Procedia Comput Sci. 2022;214:18-25. doi: 10.1016/j.procs.2022.11.143. Epub 2022 Dec 8.
PubMed 36514710Belciug S. Autonomous fetal morphology scan: deep learning + clustering merger - the second pair of eyes behind the doctor. BMC Med Inform Decis Mak. 2024 Apr 19;24(1):102. doi: 10.1186/s12911-024-02505-3.
PubMed 38641580Belciug S, Ivanescu RC, Serbanescu MS, Ispas F, Nagy R, Comanescu CM, Istrate-Ofiteru A, Iliescu DG. Pattern Recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical learning (PARADISE): protocol for the development of an intelligent decision support system using fetal morphology ultrasound scan to detect fetal congenital anomaly detection. BMJ Open. 2024 Feb 15;14(2):e077366. doi: 10.1136/bmjopen-2023-077366.
PubMed 38365300
Study at a glance
From the registry record, last updated by the sponsor on 13 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning
- ClinicalTrials.gov ID
- NCT05738954
- Acronym / sponsor ID
- PARADISE / PARADISE
- Conditions
- Congenital Abnormalities
- Keywords
- ultrasound, fetal morphology, view planes, deep learning, learning curves
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- University of CraiovaUniversity, hospital or other
- Study start
- 4 May 2022 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 22 Feb 2023 (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.
Congenital anomalies (CA) are the most encountered cause of fetal death, infant mortality and morbidity.7.9 million infants are born with CA yearly. Early detection of CA facilitates life-saving treatments and stops the progression of disabilities. CA can be diagnosed prenatally through Morphology Scan (MS). Discrepancies between pre and postnatal diagnosis of CA reach 29%. A correct interpretation of MS allows a detailed discussion regarding the prognosis with parents. The central feature of PARADISE is the development of a specialized intelligent system that embeds a committee of Deep Learning and Statistical Learning methods, which work together in a competitive/collaborative way to increase the performance of MS examinations by signaling CA. Using preclinical testing and clinical validation, the main goal will be the direct implementation into clinical practice. This multi-disciplinary project offers a unique integration of approaches, competences, breakthroughs in key applications in human, psychological, technological, and economical interest such as the 'smarter' healthcare system, opening new fields of research. PARADISE creates an environment that contributes significantly to the healthcare system, medical and pharma industries, scientific community, economy and ultimately to each individual. Its outcome will increase impact on the management of CA by enabling the establishment of detailed plans before birth, which will decrease morbidity and mortality in infants.
Detailed description
Probe guidance: The IS guides the sonographer's probe for better acquisition of the fetal biometric plane - Basic scanning to be performed by non-expert(> 90% accuracy (AC)) Fetal biometric plane finder: The fetal planes are automatically detected, measured and stored - Insurance that all anatomical parts are checked (100% AC) Anomaly detection: unusual findings are signaled - Assistance in decision making (>90% AC)
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Second trimester | – | Other: UltrasoundSecond trimester fetal morphology Collect patient data, anonymize and label it. Written informed consent or verbal recorded consent (if the participant lacks the ability to write or sign) will be obtained before performing the MS. In the unlikely event that some of the participants will withdraw their consent after the ultrasound has been performed, the data collected will not be used in the project. Data for publications and dataset will be previously made anonymous following standard practices. The participants will sign a GDPR form. |
Who may qualify
- Ages
- 18 to 50 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- The pregnant women that have their second trimester morphology scan scheduled. They are included in the study consecutively.
Key inclusion criteria (1 listed)
- Second trimester pregnant women
Key exclusion criteria (1 listed)
- -
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 |
|---|---|---|
| UltrasoundCollect patient data, anonymize and label it. Written informed consent or verbal recorded consent (if the participant lacks the ability to write or sign) will be obtained before performing the MS. In the unlikely event that some of the participants will withdraw their consent after the ultrasound has been performed, the data collected will not be used in the project. Data for publications and dataset will be previously made anonymous following standard practices. The participants will sign a GDPR form. The DL/SL algorithms will work in a competitive/collaborative way. Following the 'no-free-lunch' theorem, we shall use the competitive phase to establish the most suitable DL/SL technique for the identification and anomaly detection of each organ, and the collaborative phase to make all the algorithms work together in providing a 'second' opinion. | Other | Second trimester |
Primary outcome
Signal congenital anomalies
Time frame: 32 months
Number of congetinal anomalies found in a fetus at the second trimester morphology scan
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Sep 2023.
- Results publication: PubMed 35751202Belciug S. Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing. Comput Biol Med. 2022 Jul;146:105623. doi: 10.1016/j.compbiomed.2022.105623. Epub 2022 May 17.
- Results publication: PubMed 36514710Belciug S, Ivanescu RC, Popa SD, Iliescu DG. Doctor/Data Scientist/Artificial Intelligence Communication Model. Case Study. Procedia Comput Sci. 2022;214:18-25. doi: 10.1016/j.procs.2022.11.143. Epub 2022 Dec 8.
- Derived from this study: PubMed 38641580Belciug S. Autonomous fetal morphology scan: deep learning + clustering merger - the second pair of eyes behind the doctor. BMC Med Inform Decis Mak. 2024 Apr 19;24(1):102. doi: 10.1186/s12911-024-02505-3.
- Derived from this study: PubMed 38365300Belciug S, Ivanescu RC, Serbanescu MS, Ispas F, Nagy R, Comanescu CM, Istrate-Ofiteru A, Iliescu DG. Pattern Recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical learning (PARADISE): protocol for the development of an intelligent decision support system using fetal morphology ultrasound scan to detect fetal congenital anomaly detection. BMJ Open. 2024 Feb 15;14(2):e077366. doi: 10.1136/bmjopen-2023-077366.
- Project websiteLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. Element 1: Data Type Primary and secondary data from RGB 2D ultrasound images (4000 images) The data can be used by researchers to further improve the diagnostic of congenital anomalies. Element 2: Related Tools, Software and/or Code: There will be no specific tools for accessing data. Element 4: Data Preservation, Access, and Associated Timelines A. Repository where scientific data and metadata will be archived: www.zenodo.org When and how long the scientific data will be made available: December 2024-December 2034 Element 5: Access, Distribution, or Reuse Considerations Informed consent, anonymized data, privacy constraints and applicable ethical norms, national laws, privacy policies Element 6: Oversight of Data Management and Sharing: Renato Constantin Ivanescu- anonymizing the data and collecting it Dominic Iliescu, Rodica Nagy, Anca Ofiteru, Cristina Comanescu - gathering data Smaranda Belciug - overall supervision role for data management
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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.
Smaranda Belciug, Assoc. Prof. principal investigator
University of Craiova
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Congenital Abnormalities, run by University of Craiova at 1 site in Romania.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 13 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Romania. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Smaranda Belciug, Assoc. Prof. (729127574 ext. +40, sbelciug@inf.ucv.ro), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Signal congenital anomalies (time frame: 32 months). 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 NCT05738954, which also lists 4 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 13 Sep 2023.