Glaucomanet: A Deep-Learning Algorithm for the Diagnosis of Primary Open-Angle Glaucoma from Fundus Photographs

2021 ◽  
Author(s):  
Mingquan Lin ◽  
Bojian Hou ◽  
Lei Liu ◽  
Mae Gordon ◽  
Michael Kass ◽  
...  
Keyword(s):  
Deep Learning ◽  
Primary Open Angle Glaucoma ◽  
Learning Algorithm ◽  
Open Angle Glaucoma ◽  
Open Angle ◽  
Deep Learning Algorithm ◽  
Fundus Photographs

scholarly journals Multimodal Primary Open Angle Glaucoma Early Diagnosing Program Based on Clinical Process

2021 ◽  
Author(s):  
Jingyi Ma ◽  
Bin Lv ◽  
Yuanyuan Li ◽  
Pan Fan ◽  
Xu Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Open Angle ◽  
Fiber Layer ◽  
Visual Field Examination ◽  
Healthy Eyes

Abstract Background: Glaucoma is one of the leading causes of blinding disease. Early detection can improve patients’ quality of vision. Effectively identifying primary open angle glaucoma (POAG) using structural and functional examination is critical. Computer aided diagnosis of glaucoma requires multimodal data to find an accurate model for early glaucoma diagnosis. Methods: This study collected 87 early POAG eyes, 85 suspected POAG eyes, and 129 healthy eyes from the ophthalmology department at Second Affiliated Hospital of Harbin Medical University. Retinal nerve fiber layer thickness (RNFLt), intraocular pressure (IOP) value, visual field examination parameters and age were obtained. A powerful deep learning network segmented the FP and extracted optic nerve head (ONH) features. Machine learning classifiers (MLCs) were adopted to get the final classification results and compared with the diagnosis results of glaucoma specialists and general non-glaucoma ophthalmologists. Result: The program diagnosing early POAG, suspected POAG, and healthy eyes made overall Area Under the Curve of 0.97. Dice of optic disc and optic cup segmentation is 0.9631, 0.8435 respectively. Accuracy of the program (0.9004) is higher than general ophthalmologists (0.8195). Specificity of the program (0.9635) is higher than glaucoma specialists (0.9366).Conclusions: The program delivers superior results in diagnosing early POAG. This study’s hybrid deep learning-machine learning framework can assist with clinical decision for early POAG effectively.

Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone

2021 ◽  
pp. bjophthalmol-2020-318107
Author(s):  
Kenichi Nakahara ◽  
Ryo Asaoka ◽  
Masaki Tanito ◽  
Naoto Shibata ◽  
Keita Mitsuhashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Fundus Photography ◽  
Training Dataset ◽  
Mean Deviation ◽  
Fundus Camera ◽  
Deep Learning Algorithm ◽  
Testing Dataset ◽  
Advanced Glaucoma ◽  
Fundus Photographs

Background/aimsTo validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone.MethodsA training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC).ResultsThe AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < −12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras.ConclusionThe usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.

scholarly journals Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs

2020 ◽  
Vol 211 ◽  
pp. 123-131 ◽  
Author(s):  
Alessandro A. Jammal ◽  
Atalie C. Thompson ◽  
Eduardo B. Mariottoni ◽  
Samuel I. Berchuck ◽  
Carla N. Urata ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Fundus Photographs

Deep Learning-Based Diabetic Retinopathy Detection

2021 ◽  
Vol 11 (3) ◽  
pp. 38-48
Author(s):  
Mohamed Jebran P. ◽  
Sufia Banu
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Learning Problems ◽  
Deep Learning Algorithm ◽  
Lesion Level ◽  
Fundus Photographs

Artificial intelligence (AI) is rapidly evolving from machine learning (ML) to deep learning (DL), which has ignited particular interest in ophthalmology as well. Deep learning has been applied in ophthalmology to fundus photographs, which achieve robust classification performance in the detection of diabetic retinopathy (DR). Diabetic retinopathy is a progressive condition observed in people who have had multiple years of diabetes mellitus. This paper focuses on examining how a deep learning algorithm can be applied for the detection and classification of diabetic retinopathy, both at the image level and at the lesion level. The performance of various neural networks is summarized by taking into account the sensitivity, precision, accuracy with respect to the size of the test datasets. Deep learning problems are discussed at the end.

scholarly journals Development and validation of a deep learning algorithm using fundus photographs to predict 10-year risk of ischemic cardiovascular diseases among Chinese population

2021 ◽  
Author(s):  
Yanjun Ma ◽  
Jianhao Xiong ◽  
Yidan Zhu ◽  
Zongyuan Ge ◽  
Rong Hua ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Risk Prediction ◽  
Chinese Population ◽  
Goodness Of Fit ◽  
Medical Information ◽  
Learning Algorithm ◽  
External Validation ◽  
Deep Learning Algorithm ◽  
Fundus Photographs

Background Ischemic cardiovascular diseases (ICVD) risk predict models are valuable but limited by its requirement for multidimensional medical information including that from blood drawing. A convenient and affordable alternative is in demand. Objectives To develop and validate a deep learning algorithm to predict 10-year ICVD risk using retinal fundus photographs in Chinese population. Methods We firstly labeled fundus photographs with natural logarithms of ICVD risk estimated by a previously validated 10-year Chinese ICVD risk prediction model for 390,947 adults randomly selected (95%) from a health checkup dataset. An algorithm using convolutional neural network was then developed to predict the estimated 10-year ICVD risk by fundus images. The algorithm was validated using both internal dataset (the other 5%) and external dataset from an independent source (sample size = 1,309). Adjusted R2 and area under the receiver operating characteristic curve (AUC) were used to evaluate the goodness of fit. Results The adjusted R2 between natural logarithms of the predicted and calculated ICVD risks was 0.876 and 0.638 in the internal and external validations, respectively. For detecting ICVD risk ≥ 5% and ≥ 7.5%, the algorithm achieved an AUC of 0.971 (95% CI: 0.967 to 0.975) and 0.976 (95% CI: 0.973 to 0.980) in internal validation, and 0.859 (95% CI: 0.822 to 0.895) and 0.876 (95% CI: 0.816 to 0.837) in external validation. Conclusions The deep learning algorithm developed in the study using fundus photographs to predict 10-year ICVD risk in Chinese population had fairly good capability in predicting the risk and may have values to be widely promoted considering its advances in easy use and lower cost. Further studies with long term follow up are warranted. Keywords Deep learning, Ischemic cardiovascular diseases, risk prediction.

scholarly journals Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

2021 ◽  
Author(s):  
Rong Hua ◽  
Jianhao Xiong ◽  
Gail Li ◽  
Yidan Zhu ◽  
Zongyuan Ge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Score ◽  
Learning Algorithm ◽  
External Validation ◽  
Population Based ◽  
Validation Dataset ◽  
Internal Validation ◽  
Deep Learning Algorithm ◽  
Dementia Risk ◽  
Fundus Photographs

AbstractImportanceThe Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed.ObjectiveTo develop and validate a deep learning algorithm using retinal fundus photographs for estimating the CAIDE dementia risk score and identifying individuals with high dementia risk.DesignA deep learning algorithm trained via fundus photographs was developed, validated internally and externally with cross-sectional design.SettingPopulation-based.ParticipantsA health check-up population with 271,864 adults were randomized into a development dataset (95%) and an internal validation dataset (5%). The external validation used data from the Beijing Research on Ageing and Vessel (BRAVE) with 1,512 individuals.ExposuresThe estimated CAIDE dementia risk score generated from the algorithm.Main Outcome and MeasureThe algorithm’s performance for identifying individuals with high dementia risk was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI).ResultsThe study involved 258,305 participants (mean aged 42.1 ± 13.4 years, men: 52.7%) in development, 13,559 (mean aged 41.2 ± 13.3 years, men: 52.5%) in internal validation, and 1,512 (mean aged 59.8 ± 7.3 years, men: 37.1%) in external validation. The adjusted coefficient of determination (R2) between the estimated and actual CAIDE dementia risk score was 0.822 in the internal and 0.300 in the external validations, respectively. The algorithm achieved an AUC of 0.931 (95%CI, 0.922–0.939) in the internal validation group and 0.782 (95%CI, 0.749–0.815) in the external group. Besides, the estimated CAIDE dementia risk score was significantly associated with both comprehensive cognitive function and specific cognitive domains.Conclusions and RelevanceThe present study demonstrated that the deep learning algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population-based setting. Our findings suggest that fundus photography may be utilized as a noninvasive and more expedient method for dementia risk stratification.Key PointsQuestionCan a deep learning algorithm based on fundus images estimate the CAIDE dementia risk score and identify individuals with high dementia risk?FindingsThe algorithm developed by fundus photographs from 258,305 check-up participants could well identify individuals with high dementia risk, with area under the receiver operating characteristic curve of 0.931 in internal validation and 0.782 in external validation dataset, respectively. Besides, the estimated CAIDE dementia risk score generated from the algorithm exhibited significant association with cognitive function.MeaningThe deep learning algorithm based on fundus photographs has potential to screen individuals with high dementia risk in population-based settings.

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