Learning Deep Architectures for the Interpretation of Fetal Echocardiography

Introduction:

Worldwide, Congenital Heart Disease (CHD) is the most encountered fetal malformation. The incidence of congenital heart disease appears to be about 1 per 100 live born infants and is even higher in infants who die before term (1). Fetal echocardiography (FE) has evolved from just the description of the anatomical abnormalities of the heart toward quantitative assessment of its function, dimension and shape (2). Presently, FE is performed manually by the sonographer during the second trimester investigation. However, only half of the babies undergoing surgery within the first year of life have a prenatal detection (3), explaining the need for an improvement of the fetal cardiac assessment. Many studies showed the presence of a significant discrepancy between the pre- and postnatal diagnosis of the CHD obtained by a manually performed FE (4, 5).

Intelligent Decision Support Systems (ISs) are frameworks that have the capacity to gather and analyze data, communicate with other systems, learn from experience, and adapt according to new cases. Technically speaking, ISs are advanced machines that observe and respond to the environment that they have been exposed to using Artificial Intelligence (AI) (6). This project aims to foster a cross-fertilization of FE and ISs, which will provide an enormous potential in developing new fundamental theories and practical methods that rise above the boundaries of the disciplines involved and lead to new impactful methods that assist medical practice and discovery.

Methods and analysis:

The study to be performed is a cross-sectional study divided into two separated parts: the training part of the machine learning approaches within the proposed framework and the testing phase on previously unseen frames and eventually on actual video scans. All pregnant women in their first and second trimester are considered eligible for the study. Pregnant women will be admitted for their routine ultrasound examination and monitoring, first time between 12-13+6 weeks of pregnancy (for the first trimester anomaly scan) and / or between 18-24 weeks of pregnancy (for the second trimester anomaly scan). Two-dimensional evaluation of each fetal heart will include a cine loop sweep obtained from the from the four-chamber view plane by moving the transducer cranially towards the upper mediastinum, allowing the visualization of the following planes: four-chamber view, left and right ventricular outflow tracts, three vessels and trachea view. All video files saved from the US devices will be collected into the cloud. Each ultrasound sweep will be split into frames by the OB-GYN/Cardio (OBC) department. The Data Science / IT department (DSIT) will process the frames for obeying the anonymization regulations. For key feature identification, the frames will be grouped by the OBC into the classes that represent the plane views for each trimester. DSIT will try different state-of-the-art DL pre-trained algorithms on the data set with plane views. All the recent DL entries will be tailored and tested on the current two scenarios of key view identification and semantic segmentation. Their performance results (their prediction against the ground truth marked by the OBC) will be analyzed in comparison by means of a statistical test. The accuracy-speed equilibrium will be taken into account in the ranking of the approaches, since the system will finally perform on a video. Once a new video will be available in practice, the model chosen for the respective task will highlight the key feature or the segmented region on video and also provide a degree of confidence in its recognition. The OBC physicians will validate all the intermediary findings at frame level, as well as the meaningfulness of the video labelling and segmentation. The outcomes of the model on the first and second trimester videos of the same patient will be compared to assess the discrepancy.