Artificial Intelligence-Based Referral System for Patients With Diabetic Retinopathy

This article introduces a diabetic retinopathy screening program based on artificial intelligence that will be implemented in three hospitals in Mexico. It details the steps for the clinical integration of the system and tests preliminary convolutional neural network models based on Mexican guidelines.

[1]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[2]  H. González,et al.  EARLY DIAGNOSIS OF DIABETIC RETINOPATHY IN PRIMARY CARE , 2015 .

[3]  P. Scanlon.  The English National Screening Programme for diabetic retinopathy 2003–2016 , 2017, Acta Diabetologica.

[4]  Sang Jun Park,et al.  Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks , 2017, ArXiv.

[5]  D. Ting,et al.  Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review , 2016, Clinical & experimental ophthalmology.

[6]  Jacques Wainer,et al.  A data-driven approach to referable diabetic retinopathy detection , 2019, Artif. Intell. Medicine.

[7]  María Valeria Jiménez-Báez,et al.  Early diagnosis of diabetic retinopathy in primary care , 2015, Colombia medica.

[8]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[9]  S. Dagogo-Jack,et al.  Diabetes Mellitus in Developing Countries and Underserved Communities , 2017 .

[10]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[11]  Baocai Yin,et al.  IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. , 2020, Medical image analysis.

[12]  Danilo Montesi,et al.  Predicting Frailty Condition in Elderly Using Multidimensional Socioclinical Databases , 2018, Proceedings of the IEEE.

[14]  M. Kelly,et al.  Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value. , 2019, Spine.

[15]  A. Sevastopolsky,et al.  Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network , 2017, Pattern Recognition and Image Analysis.

[16]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[17]  Changsheng Chen,et al.  An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network , 2019, Journal of Medical Systems.

[18]  W. L. Benedict.  The International Council of Ophthalmology , 1954 .

[19]  Laude,et al.  FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE , 2014 .