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

NCT ID: NCT05738954

Last Updated: 2023-09-13

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 4000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-05-04
• Study Completion Date: 2024-12-31

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)

Conditions

Congenital Abnormalities

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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.

Ultrasound

Intervention Type: 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.

Interventions

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Second trimester pregnant women

Exclusion Criteria

-

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 50 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

University of Craiova

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Smaranda Belciug, Assoc. Prof.

Role: PRINCIPAL_INVESTIGATOR

University of Craiova

Locations

University Emergency County Hospital

Craiova, Dolj, Romania

Site Status: RECRUITING

Countries

Romania

Central Contacts

Smaranda Belciug, Assoc. Prof.

Role: CONTACT

sbelciug@inf.ucv.ro

729127574 ext. +40

Dominic G Iliescu, Assoc. Prof.

Role: CONTACT

dominic.iliescu@yahoo.com

723888773 ext. +40

Facility Contacts

Dominic G Iliescu, Assoc.Prof.

Role: primary

dominic.iliescu@yahoo.com

723888773 ext. +40

References

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.

Reference Type: RESULT

PMID: 35751202 (View on PubMed)

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.

Reference Type: RESULT

PMID: 36514710 (View on PubMed)

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.

Reference Type: DERIVED

PMID: 38641580 (View on PubMed)

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.

Reference Type: DERIVED

PMID: 38365300 (View on PubMed)

Related Links

https://www.paradise-pce.ro/

Project website

Other Identifiers

PARADISE

Identifier Type: -

Identifier Source: org_study_id