NCT05738954

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 4,000 (estimated)
• Sponsor: University of Craiova · Academic / Other
• Sex: Female
• Age: 18 Years – 50 Years
• Healthy volunteers: Accepted

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)

Conditions

• Congenital Abnormalities

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2022-05-04
• Primary completion: 2024-12-31
• Completion: 2024-12-31
• First posted: 2023-02-22
• Last updated: 2023-09-13

Locations

1 site across 1 country: Romania