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

Recruiting

• Conditions: Congenital Abnormalities
• Interventions: Other: Ultrasound

• Registration Number: NCT05738954
• Lead Sponsor: University of Craiova

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.

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 Timeline

• Study Start: 2022-05-04
• Study First Submitted: 2023-02-01
• Study First Submitted QC: 2023-02-11
• Study First Posted: 2023-02-22
• Status Verified: 2023-09
• Last Update Submitted: 2023-09-11
• Last Update Posted: 2023-09-13
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 4000

Inclusion Criteria

• Second trimester pregnant women

Exclusion Criteria

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Second trimester	Ultrasound	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.

Primary Outcome Measures

Name	Time	Method
Signal congenital anomalies	32 months	Number of congetinal anomalies found in a fetus at the second trimester morphology scan

Trial Locations

Locations (1)

University Emergency County Hospital; 🇷🇴 Craiova, Dolj, Romania