Liu Xiangyu, Zhang Yingying, Zhu Haogang, Yang Xu, Cheng Jian, Jin Jishuo, Jia Bosen, He Yihua, Zhang Hongjia
School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China.
Digit Health. 2024 Oct 14;10:20552076241286929. doi: 10.1177/20552076241286929. eCollection 2024 Jan-Dec.
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.
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.
Incorporating cyclic loss significantly enhanced the performance of deep learning networks in predicting peak points for diagnosing fetal arrhythmia, resulting in improvements ranging from 7.11% to 14.81% in 1-score across different network combinations. Particularly noteworthy was the 18.2% improvement in the 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%.
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.
胎儿心律失常常与胎儿先天性心脏病同时出现。M型胎儿超声心动图中观察到的峰值是胎儿心律失常的关键诊断标志物。然而，斑点、伪像和噪声对准确的图像分析构成了显著挑战。虽然当前的深度学习网络主要忽略了心脏周期信息，但本研究专注于整合此类特征，利用从心脏周期特征中得出的上下文约束来提高诊断准确性。
本研究提出了一种用于诊断胎儿心律失常的新型深度学习架构。该架构提出了一种针对心脏周期信息量身定制的损失函数，并制定了一种用于对胎儿心律失常进行分类的诊断算法。训练和验证过程使用了一个包含从890名参与者收集的4440个图像块的数据集。
纳入循环损失显著提高了深度学习网络在预测诊断胎儿心律失常的峰值点方面的性能，在不同网络组合中，F1分数提高了7.11%至14.81%。特别值得注意的是，低质量组的F1分数提高了18.2%。此外，对四类胎儿心律失常的诊断精度有所提高，平均提高率为20.6%。
本研究引入了一种基于心脏周期信息的循环损失机制。使用基线方法和最先进的深度学习架构与胎儿超声心动图数据集进行了比较评估。这些评估证明了所提出框架在诊断胎儿心律失常方面的卓越准确性。还需要注意的是，进一步的外部测试对于评估模型的通用性和临床价值至关重要。
