west china medical publishers
Author
  • Title
  • Author
  • Keyword
  • Abstract
Advance search
Advance search

Search

find Author "GUO Jixiang" 4 results
  • Machine learning-based diagnostic test accuracy (1): study design

    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.

    Release date: Export PDF Favorites Scan
  • Machine learning-based diagnostic test accuracy research: measurement indicators

    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.

    Release date: Export PDF Favorites Scan
  • Artificial intelligence-based diagnostic test accuracy studies: methodological quality assessment and reporting guidelines

    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.

    Release date:2024-01-02 04:10 Export PDF Favorites Scan
  • Artificial intelligence-based diagnostic test accuracy studies: methodological quality assessment and reporting guidelines

    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.

    Release date: Export PDF Favorites Scan
1 pages Previous 1 Next

Format

Content