Unsupervised learning for large-scale corneal topography clustering.

ABSTRACT: Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, most applications of machine learning in medicine are attempts to mimic or exceed human diagnostic capabilities but little work has been done to show the power of these algorithms to help collect and pre-process a large amount of data. In this study we show how unsupervised learning can extract and sort usable data from large unlabeled datasets with minimal human intervention. Our digital examination tools used in clinical practice store such databases and are largely under-exploited. We applied unsupervised algorithms to corneal topography examin

SUBMITTER: Zeboulon P 

PROVIDER: S-EPMC7550569 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature