Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning

NCT05738954 | Unknown DMC

• 1 other identifier: PARADISE
• Study Type: observational
• Target: 4,000
• Locations: 1 country
• Sites: 1

Brief 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). 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.

Conditions

Congenital Abnormalities

Keywords

ultrasound; fetal morphology; view planes; deep learning; learning curves

Outcome Measures

Primary Outcomes (1)

• Signal congenital anomalies

  Number of congetinal anomalies found in a fetus at the second trimester morphology scan

  32 months

Study Arms (1)

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.

Other: Ultrasound

Interventions

Ultrasound OTHER

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.

Second trimester

Eligibility Criteria

• Age: 18 Years - 50 Years
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

The pregnant women that have their second trimester morphology scan scheduled. They are included in the study consecutively.

You may qualify if:

• Second trimester pregnant women

Sponsors & Collaborators

• University of Craiova: lead

Study Sites (1)

University Emergency County Hospital

Craiova, Dolj, 200643, Romania

RECRUITING

Related Publications (4)

• 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.

  PMID: 35751202 RESULT

• Belciug 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.

  PMID: 36514710 RESULT

• Belciug 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.

  PMID: 38641580 DERIVED

• Belciug 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.

  PMID: 38365300 DERIVED

Related Links

• Project website

MeSH Terms

Conditions

Congenital Abnormalities

Interventions

High-Energy Shock Waves

Condition Hierarchy (Ancestors)

Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Intervention Hierarchy (Ancestors)

Ultrasonic Waves; Sound; Radiation, Nonionizing; Radiation; Physical Phenomena

Study Officials

• Smaranda Belciug, Assoc. Prof.

  University of Craiova

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Smaranda Belciug, Assoc. Prof.

CONTACT

729127574; sbelciug@inf.ucv.ro

Dominic G Iliescu, Assoc. Prof.

CONTACT

723888773; dominic.iliescu@yahoo.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 1, 2023
• First Posted: February 22, 2023
• Study Start: May 4, 2022
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: September 13, 2023

Record last verified: 2023-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, CSR
• Time Frame: The data will become available in December 2024 and will be available until December 2034
• Access Criteria: No criteria

Locations

RO (1)