Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation
Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation
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DOI：
10.1080/03091902.2023.2174198
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发表时间：
2023-04-03
影响因子：
--
通讯作者：
Shekhar, Raj
中科院分区：
文献类型：
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作者：
Arjoune, Youness;Nguyen, Trong N. N.;Shekhar, Raj
数字听诊器可以实现集成人工智能（AI）系统的开发，该系统可以消除手动听诊的主观性，提高诊断准确性，并补偿听诊技能的下降。开发可扩展的人工智能系统可能具有挑战性，特别是当采集设备不同并因此引入传感器偏差时。为了解决这个问题，需要准确了解这些差异，即，需要这些器件的频率响应，但是制造商通常不提供完整的器件规格。在这项研究中，我们报告了一种有效的方法来确定数字听诊器的频率响应，并使用它来测试三种常见的数字听诊器：Littmann 3200，Eko Core和Thinklabs One。我们的研究结果表明，三种研究听诊器的频率响应明显不同，存在显著的器械间变异性。当比较两个单独的Littmann 3200装置时，观察到中度器械内变异性。该研究强调了在开发成功的人工智能辅助听诊的设备之间进行标准化的必要性，并提供了一种技术表征方法作为实现这一目标的第一步。
Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.