Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

K Cao; K Verspoor; S Sahebjada; PN Baird

Journal article

Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

K Cao, K Verspoor, S Sahebjada, PN Baird

Journal of Clinical Medicine | Published : 2022

DOI: 10.3390/jcm11030478

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Abstract

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to eac..

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University of Melbourne Researchers

Karin Verspoor Author

Srujana Sahebjada Author

Paul Baird Author

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Grants

Awarded by National Health and Medical Research Council

Funding Acknowledgements

This research was funded by the Australian National Health and Medical Research Council (NHMRC) project Ideas grant, grant number APP1187763, Senior Research Fellowship grant number 1138585 to PNB, Lions Eye Donation Service (SS), Angior Family Foundation (SS), Perpetual Impact Philanthropy grant (SS), and Keratoconus Australia Funding (SS). The Centre for Eye Research Australia (CERA) receives Operational Infrastructure Support from the Victorian Government. The sponsor or funding organizations had no role in the design or conduct of this research.