Key result
A deep learning architecture integrating cardiac cyclic loss improved the precision of diagnosing fetal arrhythmia by an average of 20.6% compared to baseline methods.
Why the study?
M-mode fetal echocardiogram analysis for diagnosing fetal arrhythmias is challenged by speckles, artifacts, and noise, and existing deep learning networks typically overlook cardiac cyclic information.
Does a deep learning architecture with cyclic loss improve the diagnostic accuracy of fetal arrhythmias in echocardiography compared to baseline methods?
Does a deep learning architecture with cyclic loss improve the diagnostic accuracy of fetal arrhythmias in echocardiography compared to baseline methods?
A novel deep learning architecture incorporating cardiac cyclic loss improves the precision of diagnosing fetal arrhythmias from M-mode echocardiograms.
May enhance fetal arrhythmia detection amid artifacts; leaves open validation in larger prospective cohorts.
Background: Fetal arrhythmias frequently co-occur with congenital heart disease in fetuses. The peaks observed in M-mode fetal echocardiograms serve as pivotal diagnostic markers for fetal arrhythmias. However, speckles, artifacts, and noise pose notable challenges for accurate image analysis. While current deep learning networks mainly overlook cardiac cyclic information, this study concentrated on the integration of such features, leveraging contextual constraints derived from cardiac cyclical features to improve diagnostic accuracy. Methods: This study proposed a novel deep learning architecture for diagnosing fetal arrhythmias. The architecture presented a loss function tailored to the cardiac cyclical information and formulated a diagnostic algorithm for classifying fetal arrhythmias. The training and validation processes utilized a dataset comprising 4440 patches gathered from 890 participants. Results: 1-score for the low-quality group. Additionally, the precision of diagnosing fetal arrhythmia across four categories exhibited improvement, with an average improvement rate of 20.6%. Conclusion: This study introduced a cyclic loss mechanism based on the cardiac cycle information. Comparative evaluations were conducted using baseline methods and state-of-the-art deep learning architectures with the fetal echocardiogram dataset. These evaluations demonstrated the proposed framework's superior accuracy in diagnosing fetal arrhythmias. It is also crucial to note that further external testing is essential to assess the model's generalizability and clinical value.
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Liu et al. (2024) studied Fetal arrhythmias (n=890). Deep learning architecture with cyclic loss vs. Baseline methods and state-of-the-art deep learning architectures was evaluated on Diagnostic precision of fetal arrhythmia. A deep learning architecture integrating cardiac cyclic loss improved the precision of diagnosing fetal arrhythmia by an average of 20.6% compared to baseline methods.
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