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

Abstract

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.

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