A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging

Abstract

Abstract This chapter presents a regulatory science perspective on the assessment of machine learning algorithms in diagnostic imaging applications. Most of the topics are generally applicable to many medical imaging applications, while brain disease-specific examples are provided when possible. The chapter begins with an overview of US FDA’s regulatory framework followed by assessment methodologies related to ML devices in medical imaging. Rationale, methods, and issues are discussed for the study design and data collection, the algorithm documentation, and the reference standard. Finally, study design and statistical analysis methods are overviewed for the assessment of standalone performance of ML algorithms as well as their impact on clinicians (i.e., reader studies). We believe that assessment methodologies and regulatory science play a critical role in fully realizing the great potential of ML in medical imaging, in facilitating ML device innovation, and in accelerating the translation of these technologies from bench to bedside to the benefit of patients.

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