Clinical trial NCT05738954: Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning
Official title: Pattern Recognition and Anomaly Detection in Fetal Morphology Using Deep Learning and Statistical Learning
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Congenital Abnormalities
Keywords: ultrasound
fetal morphology
view planes
deep learning
learning curves
Lead sponsor: University of Craiova
Sponsor class: OTHER
First posted: 2023-02-22
Enrollment: 4000
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 50
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The pregnant women that have their second trimester morphology scan scheduled. They are included in the study consecutively.

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)

Eligibility criteria: Inclusion Criteria:

* Second trimester pregnant women

Exclusion Criteria:

-

Primary outcomes:
measure: Signal congenital anomalies
timeFrame: 32 months
Secondary outcomes:
Other outcomes:
Locations:
city: Craiova
country: Romania
facility: University Emergency County Hospital
state: Dolj
Study officials:
affiliation: University of Craiova
name: Smaranda Belciug, Assoc. Prof.
role: PRINCIPAL_INVESTIGATOR
References:
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.
citation: 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.