Buhl Mareike, Akin Gülce, Saak Samira, Eysholdt Ulrich, Radeloff Andreas, Kollmeier Birger, Hildebrandt Andrea
Department of Medical Physics and Acoustics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Front Neurol. 2022 Aug 23;13:960012. doi: 10.3389/fneur.2022.960012. eCollection 2022.
For supporting clinical decision-making in audiology, Common Audiological Functional Parameters (CAFPAs) were suggested as an interpretable intermediate representation of audiological information taken from various diagnostic sources within a clinical decision-support system (CDSS). Ten different CAFPAs were proposed to represent specific functional aspects of the human auditory system, namely hearing threshold, supra-threshold deficits, binaural hearing, neural processing, cognitive abilities, and a socio-economic component. CAFPAs were established as a viable basis for deriving audiological findings and treatment recommendations, and it has been demonstrated that model-predicted CAFPAs, with machine learning models trained on expert-labeled patient cases, are sufficiently accurate to be included in a CDSS, but it requires further validation by experts. The present study aimed to validate model-predicted CAFPAs based on previously unlabeled cases from the same data set. Here, we ask to which extent domain experts agree with the model-predicted CAFPAs and whether potential disagreement can be understood in terms of patient characteristics. To these aims, an expert survey was designed and applied to two highly-experienced audiology specialists. They were asked to evaluate model-predicted CAFPAs and estimate audiological findings of the given audiological information about the patients that they were presented with simultaneously. The results revealed strong relative agreement between the two experts and importantly between experts and the prediction for all CAFPAs, except for the neural processing and binaural hearing-related ones. It turned out, however, that experts tend to score CAFPAs in a larger value range, but, on average, across patients with smaller scores as compared with the machine learning models. For the hearing threshold-associated CAFPA in frequencies smaller than 0.75 kHz and the cognitive CAFPA, not only the relative agreement but also the absolute agreement between machine and experts was very high. For those CAFPAs with an average difference between the model- and expert-estimated values, patient characteristics were predictive of the disagreement. The findings are discussed in terms of how they can help toward further improvement of model-predicted CAFPAs to be incorporated in a CDSS for audiology.
为支持听力学中的临床决策，提出了常见听力学功能参数（CAFPAs），作为临床决策支持系统（CDSS）中从各种诊断来源获取的听力学信息的可解释中间表示。提出了十种不同的CAFPAs来代表人类听觉系统的特定功能方面，即听力阈值、阈上缺陷、双耳听力、神经处理、认知能力和社会经济成分。CAFPAs被确立为得出听力学结果和治疗建议的可行基础，并且已经证明，通过在专家标记的患者病例上训练的机器学习模型预测的CAFPAs足够准确，可以纳入CDSS，但需要专家进一步验证。本研究旨在基于同一数据集先前未标记的病例验证模型预测的CAFPAs。在此，我们询问领域专家在多大程度上同意模型预测的CAFPAs，以及潜在的分歧是否可以根据患者特征来理解。为了实现这些目标，设计了一项专家调查并应用于两位经验丰富的听力学专家。要求他们评估模型预测的CAFPAs，并估计同时呈现给他们的关于患者的给定听力学信息的听力学结果。结果显示，两位专家之间以及专家与所有CAFPAs的预测之间存在很强的相对一致性，但与神经处理和双耳听力相关的CAFPAs除外。然而，结果表明，专家倾向于在更大的值范围内对CAFPAs进行评分，但与机器学习模型相比，平均而言，患者得分较小。对于频率小于0.75kHz的听力阈值相关CAFPA和认知CAFPA，机器与专家之间不仅相对一致性高，而且绝对一致性也非常高。对于那些模型估计值与专家估计值之间存在平均差异的CAFPAs，患者特征可预测分歧。将根据这些发现如何有助于进一步改进模型预测的CAFPAs以纳入听力学CDSS来进行讨论。
