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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.
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)
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ultrasound | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Signal congenital anomalies | Number of congetinal anomalies found in a fetus at the second trimester morphology scan | 32 months |
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Inclusion Criteria:
Exclusion Criteria:
-
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The pregnant women that have their second trimester morphology scan scheduled. They are included in the study consecutively.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Smaranda Belciug, Assoc. Prof. | Contact | 729127574 | +40 | sbelciug@inf.ucv.ro |
| Dominic G Iliescu, Assoc. Prof. | Contact | 723888773 | +40 | dominic.iliescu@yahoo.com |
| Name | Affiliation | Role |
|---|---|---|
| Smaranda Belciug, Assoc. Prof. | University of Craiova | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Emergency County Hospital | Recruiting | Craiova | Dolj | 200643 | Romania |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35751202 | Result | 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. | |
| 36514710 | Result | 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. |
| Label | URL |
|---|---|
| Project website | View source |
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Element 1: Data Type Primary and secondary data from RGB 2D ultrasound images (4000 images)
The data can be used by researchers to further improve the diagnostic of congenital anomalies.
Element 2: Related Tools, Software and/or Code:
There will be no specific tools for accessing data.
Element 4: Data Preservation, Access, and Associated Timelines
A. Repository where scientific data and metadata will be archived:
When and how long the scientific data will be made available:
December 2024-December 2034
Element 5: Access, Distribution, or Reuse Considerations Informed consent, anonymized data, privacy constraints and applicable ethical norms, national laws, privacy policies
Element 6: Oversight of Data Management and Sharing:
Renato Constantin Ivanescu- anonymizing the data and collecting it Dominic Iliescu, Rodica Nagy, Anca Ofiteru, Cristina Comanescu - gathering data Smaranda Belciug - overall supervision role for data management
The data will become available in December 2024 and will be available until December 2034
No criteria
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| ID | Term |
|---|---|
| D000013 | Congenital Abnormalities |
| ID | Term |
|---|---|
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
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| ID | Term |
|---|---|
| D019220 | High-Energy Shock Waves |
| ID | Term |
|---|---|
| D000069453 | Ultrasonic Waves |
| D013016 | Sound |
| D011840 | Radiation, Nonionizing |
| D011827 | Radiation |
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| 38641580 | Derived | 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. |
| 38365300 | Derived | 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. |
| D055585 |
| Physical Phenomena |