Detection of Meibomian Gland Dysfunction by In Vivo Confocal Microscopy Using Deep Convolutional Neural Network

    October 2021 in “ Research Square (Research Square) ”
    Yi Shao, Yichen Yang, Hui Zhao … Xiangchun Li

    Preprint — not peer reviewed. This was posted to a preprint server or data repository. It has not been through a journal's review process, and its findings may change or not hold up.

    Studysummary This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.
    Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer
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    Research cited in this study 1

    1. Evaluation of Dry Eye and Meibomian Gland Dysfunction in Female Androgenetic Alopecia Patients International Ophthalmology · 2021