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Biostatistics Seminar Series: 07/13 Monday 2pm, “Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression”


Dear Colleagues,

The Division of Biostatistics at the Department of Preventive Medicine invites you to attend the following seminar.

Date: Monday, July 13th, 2020

Time: 2 P.M.

ZOOM Virtual Room Connection:  https://tennessee.zoom.us/j/99322680124

Presenter: Dr. Qi Yan of University of Pittsburgh.

Title:Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression

Abstract: Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by images of the fundus of the retina and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have used both genetic and image data for predicting AMD progression. Here we used both genotypes and fundus images to predict whether an eye had progressed to late AMD with a modified deep convolutional neural network. In total, we used 31,262 fundus images and 52 AMD-associated genetic variants from 1,351 subjects from the Age-Related Eye Disease Study, which provided disease severity phenotypes and fundus images available at baseline and follow-up visits over a period of 12 years. Our results showed that fundus images coupled with genotypes could predict late AMD progression with an averaged area-under-the-curve value of 0.85 (95% confidence interval 0.83–0.86). The results using fundus images alone showed an averaged area under the receiver operating characteristic curve value of 0.81 (95% confidence interval 0.80–0.83). We implemented our model in a cloud-based application for individual risk assessment.

Manuscript link: https://www.nature.com/articles/s42256-020-0154-9.epdf?author_access_token=ytmgnX1807mH5XIqctD70NRgN0jAjWel9jnR3ZoTv0PEfAOwkHt47te-T29RleTSe9oHXYrJmvV2kI8DSjCGvvJJ2UNPwgtcl5H-tThiXXjuj_zR8X1zt5G1gXJJEL0oiV6ttgo-2tQKy4cqFw3lQw%3D%3D

About the speaker:
Dr. Qi Yan got his Ph.D. in Biostatistics at University of Alabama at Birmingham in 2014. His primary research interests lie broadly in statistical genetics and bioinformatics. In particular, he is interested in application of cutting-edge technologies (e.g., sequencing technology) for analysis of high-throughput genetic and genomic data, and development of statistical and computational methods. Dr. Yan will start his Assistant Professor job in August in Division of Reproductive Sciences, Department of Obstetrics & Gynecology at Columbia University Medical Center.
(Speaker’s webpage: https://qiyanpitt.github.io/)

We look forward to seeing you all among us. 

Chi-Yang Chiu, Ph.D.

Assistant Professor of Biostatistics

Department of Preventive Medicine, UTHSC