scholarly journals Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images (Preprint)

2021 ◽  
Author(s):  
Xiuhua Guo
Keyword(s):  
Anomaly Detection ◽  
Sensitivity And Specificity ◽  
Clinical Pathways ◽  
Age Related Macular Degeneration ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Ocular Diseases ◽  
Data Set ◽  
Retinal Fundus Images ◽  
Retinal Fundus

BACKGROUND The supervised deep learning approach provides state-of-the-art performance in a variety of fundus image classification tasks, but it is not applicable to screening tasks with numerous or unknown disease types. The unsupervised anomaly detection (AD) approach, which needs only normal samples to develop a model, may be a workable and cost-saving method of screening for ocular diseases. OBJECTIVE To developed and evaluated an AD model for detecting ocular diseases based on color fundus images. METHODS A generative adversarial network (GAN)–based AD method for detecting possible ocular diseases was developed and evaluated using 90499 retinal fundus images derived from four large-scale real-world data sets. Three other independent external test sets were used for external testing and further analysis of the model’s performance in detecting six common ocular diseases (diabetic retinopathy (DR), glaucoma, cataract, age-related macular degeneration (AMD), hypertensive retinopathy (HR), myopia) and DR of different severity levels. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity of the model’s performance were calculated and presented. RESULTS Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in one external proprietary data set. In the detection of six common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR and myopia were 0.891, 0.916, 0.912, 0.867, 0.895 and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR and 0.926 for detecting vision-threatening DR. CONCLUSIONS The AD approach achieved high sensitivity and specificity in detecting ocular diseases based on fundus images, meaning that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. In future research, it will be necessary to evaluate the practical applicability of the AD approach in ocular disease screening.

A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images

2008 ◽  
Vol 34 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Cemal Köse ◽  
Uğur Şevik ◽  
Okyay Gençalioğlu ◽  
Cevat İkibaş ◽  
Temel Kayıkıçıoğlu
Keyword(s):  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Images ◽  
Segmentation Method ◽  
Age Related ◽  
Statistical Segmentation ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Screening diabetic retinopathy using an automated retinal image analysis (ARIA) system in Mexico: independent and assistive use cases

2020 ◽  
Author(s):  
Alejandro Noriega ◽  
Dalia Camacho ◽  
Daniela Meizner ◽  
Jennifer Enciso ◽  
Hugo Quiroz-Mercado ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Latin American ◽  
Use Cases ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Mexican Patient ◽  
Retinal Fundus

Background: The automated screening of patients at risk of developing diabetic retinopathy (DR), represents an opportunity to improve their mid-term outcome and lower the public expenditure associated with direct and indirect costs of a common sight-threatening complication of diabetes. Objective: In the present study, we aim at developing and evaluating the performance of an automated deep learning-based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, we study the performance of the automated retina image analysis (ARIA) system under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis. Methods: We ran a randomized controlled experiment where 17 ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the opinion of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the opinion of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists' opinion in each condition and the opinion of the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three retina specialists for each fundus image. Results: The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC), sensitivity, and specificity of 98%, 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, 90% respectively for Mexican patient-cases. The results achieved on Mexican patient-cases outperformed the average performance of the 17 ophthalmologist participants of the study. We also find that the ARIA system can be useful as an assistive tool, as significant specificity improvements were observed in the experimental condition where participants were exposed to the answer of the ARIA system as a second opinion (93.3%), compared to the specificity of the condition where participants assessed the images independently (87.3%). Conclusions: These results demonstrate that both use cases of ARIA systems, independent and assistive, present a substantial opportunity for Latin American countries like Mexico towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.

scholarly journals Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Mexico: Independent and Assistive use Cases (Preprint)

2020 ◽  
Author(s):  
Alejandro Noriega ◽  
Daniela Meizner ◽  
Dalia Camacho ◽  
Jennifer Enciso ◽  
Hugo Quiroz-Mercado ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Use Cases ◽  
Image Analysis System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

BACKGROUND The automated screening of patients at risk of developing diabetic retinopathy (DR) represents an opportunity to improve their mid-term outcome, and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. OBJECTIVE The present study, aims to develop and evaluate the performance of an automated deep learning–based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, the performance of the automated retina image analysis (ARIA) system is evaluated under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the three schemes. METHODS A randomized controlled experiment was performed where seventeen ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the retina image classification of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three retina specialists for each fundus image. RESULTS The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC) of 98.0% and a sensitivity and specificity of 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, and 90.0% respectively for Mexican patient-cases. The results achieved outperformed the average performance of the seventeen ophthalmologists enrolled in the study. Additionally, the achieved results suggest that the ARIA system can be useful as an assistive tool, as significant sensitivity improvements were observed in the experimental condition where ophthalmologists were exposed to the ARIA’s system answer previous to their own classification (93.3%), compared to the sensitivity of the condition where participants assessed the images independently (87.3%). CONCLUSIONS These results demonstrate that both use cases of the ARIA system, independent and assistive, present a substantial opportunity for Latin American countries like Mexico, towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.

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