scholarly journals Gender prediction via deep learning across different retinal fundus photograph fields: a multi-ethnic study (Preprint)

10.2196/25165 ◽  
2020 ◽  
Author(s):  
Bjorn Kaijun Betzler ◽  
Henrik Seung Yang Hee ◽  
Sahil Thakur ◽  
Marco Yu ◽  
Ten Cheer Quek ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fundus Photograph ◽  
Ethnic Study ◽  
Retinal Fundus

scholarly journals Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ling-Ping Cen ◽  
Jie Ji ◽  
Jian-Wei Lin ◽  
Si-Tong Ju ◽  
Hong-Jie Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Characteristic Curve ◽  
Weighted Average ◽  
Age Related Macular Degeneration ◽  
Learning Platform ◽  
Age Related ◽  
Public Data ◽  
Primary Test ◽  
Retinal Fundus

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

scholarly journals DEEP LEARNING FROM NON-INVASIVE RADIAL ARTERY BLOOD PRESSURE WAVEFORM PREDICTS CARDIOVASCULAR DISEASE EVENTS IN A MULTI-ETHNIC STUDY COHORT: THE MESA STUDY

2021 ◽  
Vol 77 (18) ◽  
pp. 3213
Author(s):  
Anthony Prisco ◽  
Alex Deakyne ◽  
Weston Upchurch ◽  
Suraj Kapa ◽  
David Jacobs ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Deep Learning ◽  
Radial Artery ◽  
Study Cohort ◽  
Pressure Waveform ◽  
Ethnic Study ◽  
Non Invasive ◽  
Blood Pressure Waveform

A deep learning framework for the detection of Plus disease in retinal fundus images of preterm infants

2021 ◽  
Author(s):  
Sivakumar Ramachandran ◽  
P. Niyas ◽  
Anand Vinekar ◽  
Renu John
Keyword(s):  
Deep Learning ◽  
Preterm Infants ◽  
Fundus Images ◽  
Learning Framework ◽  
Retinal Fundus Images ◽  
Retinal Fundus

An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-24
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Final Diagnosis ◽  
Learning Rate ◽  
Ensemble Model ◽  
Fundus Images ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

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

Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images

2020 ◽  
Vol 217 ◽  
pp. 121-130 ◽  
Author(s):  
Jooyoung Chang ◽  
Ahryoung Ko ◽  
Sang Min Park ◽  
Seulggie Choi ◽  
Kyuwoong Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiovascular Mortality ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus
Sign in / Sign up

Export Citation Format

Share Document