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

Congenital Abnormalities Clinical Trial

— PARADISE  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05738954
• Other study ID #: PARADISE
• Status: Recruiting
• Start date: May 4, 2022
• Est. completion date: December 31, 2024

Study information

• Verified date: September 2023
• Source: University of Craiova
• Contact: Smaranda Belciug, Assoc. Prof.
• Phone: 729127574
• Email: sbelciug[@]inf.ucv.ro
• Is FDA regulated: No
• Study type: Observational

Clinical 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). 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.

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)

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 4000
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years to 50 Years

Eligibility

Inclusion Criteria:

• Second trimester pregnant women Exclusion Criteria: -

Related Conditions & MeSH terms

• Congenital Abnormalities

Intervention

Other:
• Ultrasound
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.

Locations

Country	Name	City	State
Romania	University Emergency County Hospital	Craiova	Dolj

Sponsors (1)

Lead Sponsor	Collaborator
University of Craiova

Country where clinical trial is conducted

Romania, 

References & Publications (2)

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Signal congenital anomalies	Number of congetinal anomalies found in a fetus at the second trimester morphology scan	32 months

External Links

•   Project website