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Pattern Recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical learning
Smaranda Belciug1, Renato Constantin Ivanescu2, Mircea Sebastian Serbanescu3
1Department of Computer Science, University of Craiova, Craiova, Romania sbelciug@inf.ucv.ro.
Insights
This study develops an intelligent system to detect fetal congenital anomalies using AI-powered ultrasound analysis. Early detection through this system aims to improve infant outcomes and reduce mortality.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Fetal Medicine
Background:
- Congenital anomalies are a leading cause of infant mortality and morbidity.
- Early detection via prenatal morphology scans is crucial for timely intervention.
- Current interpretation of scans requires expert sonographers.
Purpose of the Study:
- To develop an intelligent system for identifying fetal congenital anomalies.
- To utilize 2D ultrasound movies from morphology scans for anomaly detection.
- To assist sonographers in probe guidance, fetal plane detection, and anomaly signaling.
Main Methods:
- A cross-sectional study involving 4000 pregnant patients over 32 months.
- Development of an AI system using deep learning and statistical learning algorithms.
- The system will undergo training for detection and validation on unseen scans, with sonographer oversight.
Main Results:
- The system aims to automatically detect fetal anatomical parts and identify anomalies.
- It will provide guidance for sonographer probe positioning for optimal image acquisition.
- The system will signal unusual findings for further investigation.
Conclusions:
- The intelligent system has the potential to enhance the accuracy and efficiency of congenital anomaly detection.
- This technology can aid in early diagnosis, improving prognosis and reducing infant mortality.
- The study adheres to ethical guidelines and will be reported following STROBE recommendations.
Introduction:
Congenital anomalies are the most encountered cause of fetal death, infant mortality and morbidity. 7.9 million infants are born with congenital anomalies yearly. Early detection of congenital anomalies facilitates life-saving treatments and stops the progression of disabilities. Congenital anomalies can be diagnosed prenatally through morphology scans. A correct interpretation of the morphology scan allows a detailed discussion with the parents regarding the prognosis. The central feature of this project is the development of a specialised intelligent system that uses two-dimensional ultrasound movies obtained during the standard second trimester morphology scan to identify congenital anomalies in fetuses.
Methods And Analysis:
The project focuses on three pillars: committee of deep learning and statistical learning algorithms, statistical analysis, and operational research through learning curves. The cross-sectional study is divided into a training phase where the system learns to detect congenital anomalies using fetal morphology ultrasound scan, and then it is tested on previously unseen scans. In the training phase, the intelligent system will learn to answer the following specific objectives: (a) the system will learn to guide the sonographer's probe for better acquisition; (b) the fetal planes will be automatically detected, measured and stored and (c) unusual findings will be signalled. During the testing phase, the system will automatically perform the above tasks on previously unseen videos.Pregnant patients in their second trimester admitted for their routine scan will be consecutively included in a 32-month study (4 May 2022-31 December 2024). The number of patients is 4000, enrolled by 10 doctors/sonographers. We will develop an intelligent system that uses multiple artificial intelligence algorithms that interact between themselves, in bulk or individual. For each anatomical part, there will be an algorithm in charge of detecting it, followed by another algorithm that will detect whether anomalies are present or not. The sonographers will validate the findings at each intermediate step.
Ethics And Dissemination:
All protocols and the informed consent form comply with the Health Ministry and professional society ethics guidelines. The University of Craiova Ethics Committee has approved this study protocol as well as the Romanian Ministry of Research Innovation and Digitization that funded this research. The study will be implemented and reported in line with the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) statement.
Trial Registration Number:
The study is registered under the name 'Pattern recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical Learning', project number 101PCE/2022, project code PN-III-P4-PCE-2021-0057.
Trial Registration:
ClinicalTrials.gov, unique identifying number NCT05738954, date of registration: 2 November 2023.
