Impact of pulsed-wave-Doppler velocity-envelope tracing techniques on classification of complete fetal cardiac cycles

Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound

Arrhythmia is a major cardiac abnormality in fetuses. Therefore, early diagnosis of arrhythmia is clinically crucial. Pulsed-wave Doppler ultrasound is a commonly used diagnostic tool for fetal arrhythmia. Its key step for diagnosis involves identifying adjacent measurable cardiac cycles (MCCs). As cardiac activity is complex and the experience of sonographers is often varied, automation can improve user-independence and diagnostic-validity. However, arrhythmias pose several challenges for automation because of complex waveform variations, which can cause major localization bias and missed or false detection of MCCs. Filtering out non-MCC anomalies is difficult because of large intra-class and small inter-class variations between MCCs and non-MCCs caused by agnostic morphological waveform variations. Moreover, rare arrhythmia cases are insufficient for classification algorithms to adequately learn discriminative features. Using only normal cases for training, we propose a novel hierarchical online contrastive anomaly detection (HOCAD) framework for arrhythmia diagnosis during test time. The contribution of this study is three-fold. First, we develop a coarse-to-fine framework inspired by hierarchical diagnostic logic, which can refine localization and avoid missed detection of MCCs. Second, we propose an online learning-based contrastive anomaly detection with two new anomaly scores, which can adaptively filter out non-MCC anomalies on a single image during testing. With these complementary efforts, we precisely determine MCCs for correct measurements and diagnosis. Third, to the best of our knowledge, this is the first reported study investigating intelligent diagnosis of fetal arrhythmia on a large-scale and multi-center ultrasound dataset. Extensive experiments on 3850 cases, including 266 cases covering three typical types of arrhythmias, demonstrate the effectiveness of the proposed framework.

An intelligent quantification system for fetal heart rhythm assessment: A multicenter prospective study

BACKGROUND

The motion relationship and time intervals of the pulsed-wave Doppler (PWD) spectrum are essential for diagnosing fetal arrhythmia. However, few technologies currently are available to automatically calculate fetal cardiac time intervals (CTIs).

OBJECTIVE

The purpose of this study was to develop a fetal heart rhythm intelligent quantification system (HR-IQS) for the automatic extraction of CTIs and establish the normal reference range for fetal CTIs.

METHODS

A total of 6498 PWD spectrums of 2630 fetuses over the junction between the left ventricular inflow and outflow tracts were recorded across 14 centers. E, A, and V waves were manually labeled by 3 experienced fetal cardiologists, with 17 CTIs extracted. Five-fold cross-validation was performed for training and testing of the deep learning model. Agreement between the manual and HR-IQS-based values was evaluated using the intraclass correlation coefficient and Spearman's rank correlation coefficient. The Jarque-Bera test was applied to evaluate the normality of CTIs' distributions, and the normal reference range of 17 CTIs was established with quantile regression. Arrhythmia subset was compared with the non-arrhythmia subset using the Mann-Whitney U test.

RESULTS

Significant positive correlation (P <.001) and moderate-to-excellent consistency (P <.001) between the manual and HR-IQS automated measurements of CTIs was found. The distribution of CTIs was non-normal (P <.001). The normal range (2.5th to 97.5th percentiles) was successfully established for the 17 CTIs.

CONCLUSIONS

Using our HR-IQS is feasible for the automated calculation of CTIs in practice and thus could provide a promising tool for the assessment of fetal rhythm and function.

Applications of artificial intelligence in obstetrics

Artificial intelligence, which has been applied as an innovative technology in multiple fields of healthcare, analyzes large amounts of data to assist in disease prediction, prevention, and diagnosis, as well as in patient monitoring. In obstetrics, artificial intelligence has been actively applied and integrated into our daily medical practice. This review provides an overview of artificial intelligence systems currently used for obstetric diagnostic purposes, such as fetal cardiotocography, ultrasonography, and magnetic resonance imaging, and demonstrates how these methods have been developed and clinically applied.