scholarly journals Iterative registration for multi‐modality retinal fundus photographs using directional vessel skeleton

2021 ◽  
Vol 15 (3) ◽  
pp. 696-704
Author(s):  
Wenwen Kong ◽  
Pengxiao Zang ◽  
Sijie Niu ◽  
Dengwang Li
Keyword(s):  
Fundus Photographs ◽  
Retinal Fundus ◽  
Iterative Registration

Diabetic Retinopathy Analysis Based on Retinal Fundus Photographs via Deep Learning

2021 ◽  
pp. 61-76
Author(s):  
Yufang Tang ◽  
Yan Xu
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Fundus Photographs ◽  
Retinal Fundus

scholarly journals Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0233166
Author(s):  
Li Zhang ◽  
Mengya Yuan ◽  
Zhen An ◽  
Xiangmei Zhao ◽  
Hui Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chronic Diseases ◽  
Cross Sectional Study ◽  
Central China ◽  
Sectional Study ◽  
Cross Sectional ◽  
Fundus Photographs ◽  
Retinal Fundus

scholarly journals Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 4 ◽  
Author(s):  
Feng Li ◽  
Zheng Liu ◽  
Hua Chen ◽  
Minshan Jiang ◽  
Xuedian Zhang ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Deep Learning Algorithm ◽  
Fundus Photographs ◽  
Retinal Fundus

Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis

2020 ◽  
Vol 191 ◽  
pp. 105320 ◽  
Author(s):  
Md Mohaimenul Islam ◽  
Hsuan-Chia Yang ◽  
Tahmina Nasrin Poly ◽  
Wen-Shan Jian ◽  
Yu-Chuan (Jack) Li
Keyword(s):  
Systematic Review ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Fundus Photographs ◽  
Retinal Fundus

Artificial Intelligence Using Deep Learning in Classifying Side of the Eyes and Width of Field for Retinal Fundus Photographs

2019 ◽  
pp. 309-315
Author(s):  
Valentina Bellemo ◽  
Michelle Yuen Ting Yip ◽  
Yuchen Xie ◽  
Xin Qi Lee ◽  
Quang Duc Nguyen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Fundus Photographs ◽  
Retinal Fundus

scholarly journals Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning

2021 ◽  
Author(s):  
Edward Korot ◽  
Nikolas Pontikos ◽  
Xiaoxuan Liu ◽  
Siegfried K Wagner ◽  
Livia Faes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Model Performance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Fundus Photographs ◽  
The Uk ◽  
Retinal Fundus

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

scholarly journals Retinal fundus photographs capture hemoglobin loss after blood donation

2022 ◽  
Author(s):  
Akinori Mitani ◽  
Ilana Traynis ◽  
Preeti Singh ◽  
Greg S Corrado ◽  
Dale R Webster ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Donation ◽  
Hemoglobin Concentration ◽  
Blood Hemoglobin ◽  
Before And After ◽  
Fundus Photographs ◽  
Donated Blood ◽  
Retinal Fundus ◽  
Deep Learning Model ◽  
Blood Hemoglobin Concentration

Recently it was shown that blood hemoglobin concentration could be predicted from retinal fundus photographs by deep learning models. However, it is unclear whether the models were quantifying current blood hemoglobin level, or estimating based on subjects' pretest probability of having anemia. Here, we conducted an observational study with 14 volunteers who donated blood at an on site blood drive held by the local blood center (ie, at which time approximately 10% of their blood was removed). When the deep learning model was applied to retinal fundus photographs taken before and after blood donation, it detected a decrease in blood hemoglobin concentration within each subject at 2-3 days after donation, suggesting that the model was quantifying subacute hemoglobin changes instead of predicting subjects' risk. Additional randomized or controlled studies can further validate this finding.

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