Implications of clinical variability on computer-aided lung auscultation classification.
Implications of clinical variability on computer-aided lung auscultation classification.
复制标题
临床变异对计算机辅助肺部听诊分类的影响。
DOI：
10.1109/embc48229.2022.9871393
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Elhilali,Mounya
中科院分区：
文献类型：
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作者：
Kala,Annapurna;McCollum,EricD;Elhilali,Mounya
由于数字听诊器的最新进展和深度学习技术的快速采用，计算机听诊分析（CAA）领域取得了巨大进展。尽管有这些有希望的飞跃，但由于正确解释临床数据（特别是听诊）的固有挑战，这些技术在现实应用中的部署仍然有限。限制因素之一是临床意见的可变性带来的固有模糊性，即使是来自训练有素的专家。在开发机器学习技术以自动筛查正常与异常肺部信号时，专家意见缺乏一致性常常被忽视，大多数算法都是在高度精心策划的数据集上开发和测试的。为了更好地理解这种选择性分析在部署中可能导致的潜在陷阱，目前的工作探讨了临床意见变异性对算法的影响，以便在金标准数据上训练时检测肺音中的偶然模式。研究表明，临床意见的不确定性比专家判断中的异议引入了更多的变异性和性能下降。该研究还探讨了根据其估计的不确定性自动标记听诊信号的可行性，从而建议进一步重新评估以及改进计算机辅助分析。
Thanks to recent advances in digital stethoscopes and rapid adoption of deep learning techniques, there has been tremendous progress in the field of Computerized Auscultation Analysis (CAA). Despite these promising leaps, the deploy-ment of these technologies in real-world applications remains limited due to inherent challenges with properly interpreting clinical data, particularly auscultations. One of the limiting factors is the inherent ambiguity that comes with variability in clinical opinion, even from highly trained experts. The lack of unanimity in expert opinions is often ignored in developing machine learning techniques to automatically screen normal from abnormal lung signals, with most algorithms being developed and tested on highly curated datasets. To better understand the potential pitfalls this selective analysis could cause in deployment, the current work explores the impact of clinical opinion variability on algorithms to detect adventitious patterns in lung sounds when trained on gold-standard data. The study shows that uncertainty in clinical opinion introduces far more variability and performance drop than dissidence in expert judgments. The study also explores the feasibility of automatically flagging auscultation signals based on their estimated uncertainty, thereby recommending further reassessment as well as improving computer-aided analysis.