Development and Validation of a Deep Learning –Based  System for Detection of AIDS- Related  Cytomegalovirus Retinitis in Ultra-Wide-Field Fundus Images

2021 ◽  
Author(s):  
Kui-Fang Du ◽  
Li Dong ◽  
Kai Zhang ◽  
Chao Chen ◽  
Lian-Yong Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cytomegalovirus Retinitis ◽  
Wide Field ◽  
Fundus Images ◽  
Development And Validation

scholarly journals Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images

Ophthalmology ◽  
2020 ◽  
Vol 127 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Jaemin Son ◽  
Joo Young Shin ◽  
Hoon Dong Kim ◽  
Kyu-Hwan Jung ◽  
Kyu Hyung Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fundus Images ◽  
Learning Models ◽  
Retinal Fundus Images ◽  
Development And Validation ◽  
Retinal Fundus

scholarly journals Assessment of patient specific information in the wild on fundus photography and optical coherence tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marion R. Munk ◽  
Thomas Kurmann ◽  
Pablo Márquez-Neila ◽  
Martin S. Zinkernagel ◽  
Sebastian Wolf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Sections ◽  
Underlying Disease ◽  
Geographic Atrophy ◽  
Clinical Population ◽  
Fundus Photography ◽  
Peak Performance ◽  
Patient Specific ◽  
Specific Information ◽  
Fundus Images

AbstractIn this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient’s age and sex from fundus images, OCT cross sections and OCT volumes. For sex prediction, a ROC AUC of 0.80 was achieved for fundus images, 0.84 for OCT cross sections and 0.90 for OCT volumes. Age prediction mean absolute errors of 6.328 years for fundus, 5.625 years for OCT cross sections and 4.541 for OCT volumes were observed. We assess the performance of OCT scans containing different biomarkers and note a peak performance of AUC = 0.88 for OCT cross sections and 0.95 for volumes when there is no pathology on scans. Performance drops in case of drusen, fibrovascular pigment epitheliuum detachment and geographic atrophy present. We conclude that deep learning based methods are capable of classifying the patient’s sex and age from color fundus photography and OCT for a broad spectrum of patients irrespective of underlying disease or image quality. Non-random sex prediction using fundus images seems only possible if the eye fovea and optic disc are visible.

scholarly journals Deep Learning for Diabetic Retinopathy Detection and Classification Based on Fundus Images: A Review

2021 ◽  
pp. 104599
Author(s):  
Nikos Tsiknakis ◽  
Dimitris Theodoropoulos ◽  
Georgios Manikis ◽  
Emmanouil Ktistakis ◽  
Ourania Boutsora ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Fundus Images
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