论文标题
基于深度学习的房颤分类中风险评估的不确定性估计框架
An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification
论文作者
论文摘要
心房颤动（AF）是最常见的心律失常类型之一，仅在美国就遭受了300万人的困扰。据估计，AF是四分之一的死亡原因。人工智能（AI）算法的最新进展已导致能够可靠地从ECG信号中检测AF。尽管这些算法可以高精度准确地检测AF，但离散和确定性的分类意味着这些网络可能会错误地对给定的ECG信号分类。本文提出了一个变异自动编码器分类器网络，该网络除了可靠的分类精度外，还提供了网络输出的不确定性估计。该框架可以通过为使用基于AI的AF检测算法的信任提高医生的信任，从而提供置信度得分，从而反映了算法对案例的不确定程度，并建议他们更多地注意具有较低置信度得分的案例。不确定性是通过通过网络进行多次输入来构建分布来估计的。标准偏差的平均值被报告为网络的不确定性。除报告不确定性外，我们提议的网络还获得了97.64％的精度
Atrial Fibrillation (AF) is among one of the most common types of heart arrhythmia afflicting more than 3 million people in the U.S. alone. AF is estimated to be the cause of death of 1 in 4 individuals. Recent advancements in Artificial Intelligence (AI) algorithms have led to the capability of reliably detecting AF from ECG signals. While these algorithms can accurately detect AF with high precision, the discrete and deterministic classifications mean that these networks are likely to erroneously classify the given ECG signal. This paper proposes a variational autoencoder classifier network that provides an uncertainty estimation of the network's output in addition to reliable classification accuracy. This framework can increase physicians' trust in using AI-based AF detection algorithms by providing them with a confidence score which reflects how uncertain the algorithm is about a case and recommending them to put more attention to the cases with a lower confidence score. The uncertainty is estimated by conducting multiple passes of the input through the network to build a distribution; the mean of the standard deviations is reported as the network's uncertainty. Our proposed network obtains 97.64% accuracy in addition to reporting the uncertainty
