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

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 each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificit

SUBMITTER: Cao K 

PROVIDER: S-EPMC8836961 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature