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

Sponsor

University of Craiova (Other)

Overall Status

Recruiting

CT.gov ID

NCT05738954

Collaborator

(none)

4,000

Enrollment

Location

31.9

Anticipated Duration (Months)

125.3

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Congenital Abnormalities	Other: Ultrasound

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

Study Type:

Observational

Anticipated Enrollment :

4000 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

May 4, 2022

Anticipated Primary Completion Date :

Dec 31, 2024

Anticipated Study Completion Date :

Dec 31, 2024

Arms and Interventions

Arm	Intervention/Treatment
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 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.

Arm

Intervention/Treatment

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

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.

Outcome Measures

Primary Outcome Measures

Signal congenital anomalies [32 months]
Number of congetinal anomalies found in a fetus at the second trimester morphology scan

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 50 Years

Sexes Eligible for Study:

Female

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

Second trimester pregnant women

Exclusion Criteria:

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	University Emergency County Hospital	Craiova	Dolj	Romania	200643

Sponsors and Collaborators

University of Craiova

Investigators

Principal Investigator: Smaranda Belciug, Assoc. Prof., University of Craiova

Study Documents (Full-Text)

None provided.

More Information

Additional Information:

Project website

Publications

None provided.

Responsible Party:

University of Craiova

ClinicalTrials.gov Identifier:

NCT05738954

Other Study ID Numbers:

PARADISE

First Posted:

Feb 22, 2023

Last Update Posted:

Feb 22, 2023

Last Verified:

Feb 1, 2023

Individual Participant Data (IPD) Sharing Statement:

Undecided

Plan to Share IPD:

Undecided

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by University of Craiova

Additional relevant MeSH terms:

Congenital Abnormalities

Study Results

No Results Posted as of Feb 22, 2023