Risk-based evaluation of machine learning-based classification methods used for medical devices.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Martin Haimerl, Christoph Reich
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引用次数: 0

Abstract

Background: In the future, more medical devices will be based on machine learning (ML) methods. In general, the consideration of risks is a crucial aspect for evaluating medical devices. Accordingly, risks and their associated costs should be taken into account when assessing the performance of ML-based medical devices. This paper addresses the following three research questions towards a risk-based evaluation with a focus on ML-based classification models.

Methods: First, we analyzed how often risk-based metrics are currently utilized in the context of ML-based classification models. This was performed using a literature research based on a sample of recent scientific publications. Second, we introduce an approach for evaluating such models where expected risks and associated costs are integrated into the corresponding performance metrics. Additionally, we analyze the impact of different risk ratios on the resulting overall performance. Third, we elaborate how such risk-based approaches relate to regulatory requirements in the field of medical devices. A set of use case scenarios were utilized to demonstrate necessities and practical implications, in this regard.

Results: First, it was shown that currently most scientific publications do not include risk-based approaches for measuring performance. Second, it was demonstrated that risk-based considerations have a substantial impact on the outcome. The relative increase of the resulting overall risks can go up to 196% when the ratio between different types of risks (false negatives vs. false positives) changes by a factor of 10.0. Third, we elaborated that risk-based considerations need to be included into the assessment of ML-based medical devices, according to the relevant EU regulations and standards. In particular, this applies when a substantial impact on the clinical outcome / in terms of the risk-benefit relationship occurs.

Conclusion: In summary, we demonstrated the necessity of a risk-based approach for the evaluation of medical devices which include ML-based classification methods. We showed that currently many scientific papers in this area do not include risk considerations. We developed basic steps towards a risk-based assessment of ML-based classifiers and elaborated consequences that could occur, when these steps are neglected. And, we demonstrated the consistency of our approach with current regulatory requirements in the EU.

用于医疗器械的基于机器学习的分类方法的风险评估。
背景:未来,更多的医疗设备将基于机器学习(ML)方法。一般来说,考虑风险是评估医疗器械的一个关键方面。因此,在评估基于ml的医疗设备的性能时,应考虑风险及其相关成本。本文以基于ml的分类模型为重点,对基于风险的评估进行了以下三个研究问题。方法:首先,我们分析了基于风险的度量目前在基于ml的分类模型中使用的频率。这是通过基于最近科学出版物样本的文献研究来完成的。其次,我们引入了一种评估这些模型的方法,其中预期风险和相关成本被集成到相应的绩效指标中。此外,我们还分析了不同风险比率对最终整体绩效的影响。第三,我们详细阐述了这种基于风险的方法如何与医疗设备领域的监管要求相关。在这方面,使用了一组用例场景来演示必要性和实际含义。结果:首先,研究表明,目前大多数科学出版物不包括基于风险的方法来衡量绩效。其次,研究表明,基于风险的考虑对结果有重大影响。当不同类型的风险(假阴性与假阳性)之间的比率变化10.0倍时,由此产生的总体风险的相对增加可高达196%。第三,根据欧盟相关法规和标准,阐述了在对ml医疗器械进行评估时需要考虑风险因素。特别是,当对临床结果/风险-收益关系产生重大影响时,这一点适用。结论:总之,我们证明了基于风险的医疗器械评估方法的必要性,其中包括基于ml的分类方法。我们发现,目前这一领域的许多科学论文都没有考虑到风险。我们开发了对基于ml的分类器进行基于风险评估的基本步骤,并详细阐述了当这些步骤被忽视时可能发生的后果。此外,我们还展示了我们的方法与欧盟现行监管要求的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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