With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.
Machine learning-based diagnostic tests have certain differences of measurement indicators with traditional diagnostic tests. In this paper, we elaborate the definitions, calculation methods and statistical inferences of common measurement indicators of machine learning-based diagnosis models in detail. We hope that this paper will be helpful for clinical researchers to better evaluate machine learning diagnostic models.
This paper summarizes the methodological quality assessment tools of artificial intelligence-based diagnostic test accuracy studies, and introduces QUADAS-AI and modified QUADAS-2. Moreover, this paper summarizes reporting guidelines of these studies as well, and then introduces specific reporting standards in AI-centred research, and checklist for AI in dental research.
This paper summarizes the methodological quality assessment tools of artificial intelligence-based diagnostic test accuracy studies, and introduces QUADAS-AI and modified QUADAS-2. Moreover, this paper summarizes reporting guidelines of these studies as well, and then introduces specific reporting standards in AI-centred research, and checklist for AI in dental research.