Application of neural networks in prenatal diagnosis of atrioventricular septal defect.

Background: There is no relevant study on landmarks detection, one of the Convolutional Neural Network algorithms, in the field of fetal echocardiography (FE). This study aimed to explore whether automatic landmarks detection could be used in FE correctly and whether the atrial length (AL) to ventricular length (VL) ratio (AVLR) could be used to diagnose atrioventricular septal defect (AVSD) prenatally. Methods: This was an observational study. Two hundred and seventy-eight four-chamber views in end diastole, divided into the normal, AVSD, and differential diagnosis groups, were retrospectively included in this study. Seven landmarks were labeled sequentially by the experts on these images, and all images were divided into the training and test sets for normal, AVSD, and differential diagnosis groups. U-net, MA-net, and Link-net were used as landmark prediction neural networks. The accuracy of the landmark detection, AL, and VL measurements, as well as the prenatal diagnostic effectiveness of AVLR for AVSD, was compared with the expert labeled. Results: U-net, MA-net, and Link-net could detect the landmarks precisely (within the localization error of 0.09 and 0.13 on X and Y axis) and measure AL and VL accurately (the measured pixel distance error of AL and VL were 0.12 and 0.01 separately). AVLR in AVSD was greater than in other groups (P<0.0001), but the statistical difference was not obvious in the complete, partial, and transitional subgroups (P>0.05). The diagnostic effectiveness of AVLR calculated by three models, area under receiver operating characteristic curve could reach 0.992 (0.968-1.000), was consistent with the expert labeled. Conclusions: U-net, Link-net, and MA-net could detect landmarks and make the measurements accurately. AVLR calculated by three neural networks could be used to make the prenatal diagnosis of AVSD.

Diagnosis of fetal total anomalous pulmonary venous connection based on the post-left atrium space ratio using artificial intelligence.

OBJECTIVE: To explore whether the post-left atrium space (PLAS) ratio would be useful for prenatal diagnosis of total anomalous pulmonary venous connection (TAPVC) using echocardiography and artificial intelligence. METHODS: We retrospectively included 642 frames of four-chamber views from 319 fetuses (32 with TAPVC and 287 without TAPVC) in end-systolic and end-diastolic periods with multiple apex directions. The average gestational age was 25.6 ± 2.7 weeks. No other cardiac or extracardiac malformations were observed. The dataset was divided into a training set (n = 540; 48 with TAPVC and 492 without TAPVC) and test set (n = 102; 20 with TAPVC and 82 without TAPVC). The PLAS ratio was defined as the ratio of the epicardium-descending aortic distance to the center of the heart-descending aortic distance. Supervised learning was used in DeepLabv3+, FastFCN, PSPNet, and DenseASPP segmentation models. The area under the curve (AUC) was used on the test set. RESULTS: Expert annotations showed that this ratio was not related to the period or apex direction. It was higher in the TAPVC group than in the control group detected by the expert and the four models. The AUC of expert annotations, DeepLabv3+, FastFCN, PSPNet, and DenseASPP were 0.977, 0.941, 0.925, 0.856, and 0.887, respectively. CONCLUSION: Segmentation models achieve good diagnostic accuracy for TAPVC based on the PLAS ratio.

Simultaneous Segmentation of Four Cardiac Chambers in Fetal Echocardiography.

Accurate segmentation of cardiac chambers is helpful for the diagnosis of Congenital Heart Disease (CHD) in fetal echocardiography. Previous studies mainly focused on single cardiac chamber segmentation, which cannot provide sufficient information for the cardiologists. In this paper, we present an instance segmentation approach capable of segmenting four cardiac chambers accurately and simultaneously. A novel object proposal recovery strategy is further deployed to retrieve possible missing objects. To alleviate the shortage of medical data and further improve the segmentation performance, we utilize a rotation and distortion method for data augmentation. Experiments on a fetal echocardiography dataset of 319 fetuses demonstrate that the proposed approach can achieve superior performance according to common-used evaluation metrics.Clinical relevance-This can be used to help the cardiologists to better analyze the structure and function of the fetal heart.

Differential diagnosis of fetal large ventricular septal defect and tetralogy of Fallot based on big data analysis.

BACKGROUND AND OBJECTIVES: To analyze echocardiographic parameters of fetal large ventricular septal defect (VSD) and tetralogy of Fallot (TOF) in the context of multicenter data and big data analysis because these two diseases are often misdiagnosed in fetuses, and to find the key parameters for the differential diagnosis of these two diseases. METHODS: A total of 305 cases of large VSD and 192 cases of TOF diagnosed by fetal echocardiography from August 2010 to July 2016 from the database of Beijing Key Laboratory of Fetal Heart Defects were analyzed. Quantile regression of the 48 echocardiographic parameters of the 6272 normal fetuses from seven Chinese medical institutions was performed to determine the Q-score. The forward selection method and the naive Bayesian classification method were used to analyze the core differential diagnostic variables of fetal TOF and VSD. RESULTS: The Q-score of the internal diameter of the aorta (AO Q-score), the ratio of the diameter of the pulmonary artery to the internal diameter of the aorta (PA/AO), and the Q-score of the ratio of the diameter of the pulmonary artery to the internal diameter of the aorta (PA/AO Q-score) were key parameters for the differential diagnosis of fetal large VSD and TOF. PA/AO was the primary parameter, with an area under the receiver operating characteristic curve of 0.951. CONCLUSIONS: These findings provide a new method for the prenatal diagnosis of large VSD and TOF and a theoretical basis for the intelligent diagnosis of large VSD and TOF.