## 4 Discussion  
Fetal cardiac ultrasound assessments of an affected pregnancy should be performed sufficiently early to provide time for a proper treatment if needed. The importance of fetal cardiac ultrasound screening, incorporating multiple views of the heart and blood vessels, has been advocated to improve the prenatal detection rate for CHD [8]. Recent advances \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ConvAE-1 frame** & **ConvAE** & **AE + Global Feature** & **AnoGAN** & **SONO** \\ \hline Heart & 0.747 & 0.517 & 0.656 & 0.656 & 0.787 \\ Vessels & 0.706 & 0.542 & 0.673 & 0.651 & 0.891 \\ \hline \multicolumn{5}{l}{ConvAE, convolutional autoencoder; AnoGAN, anomaly detection with generative adversarial networks; SONO, supervised object detection with normal data only.} \\ \end{tabular}
\end{table} 
Table 2: The areas under the receiver operating characteristic curves (AUCs) for SONO and the other algorithms in Heart and Vessels. Figure 5: Receiver operating characteristic (ROC) curves showing performance comparison of SONO and the four conventional anomaly detection algorithms in detection of cardiac structural abnormalities in (**a**) Heart and (**b**) Vessels. in computer processing and transducer technology have also expanded the capacity of fetal ultrasound to include a wide variety of new modalities and sophisticated measures for cardiac structure and function. Nevertheless, the detection rate remains inaccurate and dependent on the type of ultrasound practice and experience of the examiners [24, 25]. Previous experience with CHDs and exposure to practical advice and feedback from experts, cardiologists, and cardiovascular surgeons are necessary to become a well-qualified examiner. The manual operation adds to the practical difficulties of normalizing the sweep scanning techniques and the resulting images. The research and development of the modalities with fixed patient or subject and constant measurement time, including computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and pathological images, have led to advances in high quality controls [26, 27]. However, the characteristic issues in ultrasound described above have slowed the progress of research, and there have been few publications and products associated with deep learning-based analyses of ultrasound images compared to other modalities [28, 29, 30]. Some models to support CHD screening by detecting the standardized transverse scanning planes have been reported, but the robustness of their input data needs to be considered [12, 13, 14]. We investigated deep learning using relatively small and incomplete datasets. The low incidence rate of CHD limited our ability to collect large volumes of relevant ultrasound images or videos for deep learning training. On the other hand, most pregnant women have a singleton fetus with a normal heart, among which there is little structural atypia. Therefore, we developed a novel application of object detection supervised from the dataset of normal cases only, to detect fetal cardiac substructures and structural abnormalities in fetal cardiac ultrasound screening. We analyzed fetal ultrasound videos, which consisted of the informative sequential cross-sections in an examiner-independent manner. For quality control, a high quality expert assisted in addressing the technical variety of annotation of the 18 different anatomical substructures. Our proposed SONO achieved a high detection ability, whereas the detail of their AP distribution implied that there were the detectable and undetectable substructures. Relatively small substructures such as a tricuspid valve, mitral valve, pulmonary vein, and ductus arteriosus were undetectable. We converted the video data into a barcode-like timeline. Enhancing the perspicuity of the whole examination, the barcode-like timeline made it easy to identify which substructures affected the diagnosis and hence, shorten the confirmation time.