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Background Congenital Heart Disease represents the most frequent fetal malformation. The lack of prenatal identification of congenital heart defects can have adverse consequences for the neonate, while a correct prenatal diagnosis of specific cardiac anomalies improves neonatal care neurologic and surgery outcomes. Sonographers perform prenatal diagnosis manually during the first or second-trimester scan, but the reported detection rates are low. This project’s primary objective is to develop an Intelligent Decision Support System that uses two-dimensional video files of cardiac sweeps obtained during the standard first-trimester fetal echocardiography (FE) to signal the presence/absence of previously learned key features. Methods The cross-sectional study will be divided into a training part of the machine learning approaches and the testing phase on previously unseen frames and eventually on actual video scans. Pregnant women in their 12–13 + 6 weeks of gestation admitted for routine first-trimester anomaly scan will be consecutively included in a two-year study, depending on the availability of the experienced sonographers in early fetal cardiac imaging involved in this research. The Data Science / IT department (DSIT) will process the key planes identified by the sonographers in the two- dimensional heart cine loop sweeps: four-chamber view, left and right ventricular outflow tracts, three vessels, and trachea view. The frames will be grouped into the classes representing the plane views, and then different state-of-the- art deep-learning (DL) pre-trained algorithms will be tested on the data set. The sonographers will validate all the intermediary findings at the frame level and the meaningfulness of the video labeling. Discussion FE is feasible and efficient during the first trimester. Still, the continuous training process is impaired by the lack of specialists or their limited availability. Therefore, in our study design, the sonographer benefits from a second opinion provided by the developed software, which may be very helpful, especially if a more experienced colleague is unavailable. In addition, the software may be implemented on the ultrasound device so that the process could take place during the live examination. Trial registration The study is registered under the name „Learning deep architectures for the Interpretation of Fetal Echocardiography (LIFE)”, project number 408PED/2020, project code PN-III-P2–2.1-PED-2019. Trial registration: ClinicalTrials.gov, unique identifying number NCT05090306, date of registration 30.10.2020.

Objective: In this pilot study, we tested the feasibility of cardiac structures reconstruction from histological sections in 12–13 weeks normal fetuses. Conventional autopsy is hampered at this gestational age because of the small size of the heart anatomical structures, while alternative non-invasive methods for pathology examination of the fetus are expensive, rarely available and lack accuracy data regarding the confirmation of first trimester heart defects suspected by early prenatal ultrasound (US) scans. Materials and Methods: Normal hearts from fetuses aged 12–13 gestational weeks (GW) were harvested for histological preparation, virtual reconstruction, and cardiac structures analysis. The normalcy of heart structures was confirmed before pregnancy termination, using a detailed US scan protocol. The fetal heart was routinely processed for formalin fixation and paraffin embedding (FFPE) and 10 μm seriate sections have been cut until finishing the specimen. All sections have been scanned and a three-dimensional (3D) reconstruction of the whole organ has been rendered, based on computer-aided manual tracing. Using the 3D navigation software, the main cardiac structures were searched for a proper and confident visualization. Results: Five cases were investigated. Visualization of the normal heart cavities, including atrioventricular septum was very good in all fetuses. The entire course of right and left ventricle outflow tracts was confidently confirmed, along the branching pattern of aorta and pulmonary artery trunk. Regarding the veno-atrial connections, it was easy to identify the entrance of the inferior and superior caval veins into the right atrium, but a detailed review of the histological sections was necessary for the visualization of the left atrium venous openings. The inherent morphological deformation following heart block sectioning resulted in a lower resolution or quality of the “reconstructed” planes, but these distortions did not represent a significant impediment in any of the cases. The resources involved ordinary histology and information technology (IT) equipment. To further decrease the time involved by the protocol, many steps may be automated: cutting, coloring, and scanning. Conclusions: The results indicate that this method can be implemented to routine clinical practice. The use of 3D reconstruction of fetal heart histological sections in first trimester may serve as an important audit to confirm the normalcy of heart structures. Also, the histological and postprocessed information is retained, and this volume can be stored, reanalyzed, or sent online for a second opinion. The method involves relatively undemanding resources, i.e., hardware, software, competences, and time. The procedure could also benefit from refinements used in other imaging techniques to limit human–computer interactions, such as sections distortion.