TY - JOUR
T1 - Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health
T2 - A Review
AU - Wu, Jo Hsuan
AU - Liu, Tin Yan Alvin
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2023/1
Y1 - 2023/1
N2 - The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images—most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, “super-human” predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes.
AB - The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images—most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, “super-human” predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes.
KW - artificial intelligence
KW - cardiovascular diseases
KW - color fundus photograph
KW - deep learning
KW - machine learning
KW - neurodegenerative diseases
KW - oculomics
KW - optical coherence tomography
KW - retinal imaging
KW - systemic diseases
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U2 - 10.3390/jcm12010152
DO - 10.3390/jcm12010152
M3 - Review article
C2 - 36614953
AN - SCOPUS:85145896383
SN - 2077-0383
VL - 12
JO - Journal of Clinical Medicine
JF - Journal of Clinical Medicine
IS - 1
M1 - 152
ER -