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Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence

Ramachandran Rajalakshmi, Radhakrishnan Subashini, Ranjit Mohan Anjana, Viswanathan Mohan
2018 Eye (London. 1987)  
Conclusions Automated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes  ...  Objectives To assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based  ...  Acknowledgements We acknowledge the help of all the optometrists at DMDSC for performing the digital retinal colour photography for all the patients with Remidio Fundus on phone camera and the ophthalmologists  ... 
doi:10.1038/s41433-018-0064-9 pmid:29520050 pmcid:PMC5997766 fatcat:nyasua4xgffwxplwv7crxk5dk4

Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening

Ramachandran Rajalakshmi, Subramanian Arulmalar, Manoharan Usha, Vijayaraghavan Prathiba, Khaji Syed Kareemuddin, Ranjit Mohan Anjana, Viswanathan Mohan, William H. Merigan
2015 PLoS ONE  
Aim To evaluate the sensitivity and specificity of "fundus on phone' (FOP) camera, a smartphone based retinal imaging system, as a screening tool for diabetic retinopathy (DR) detection and DR severity  ...  Grading of DR was performed by two independent retina specialists using modified Early Treatment of Diabetic Retinopathy Study grading system.  ...  Valli in recruitment of the patients in the study. We acknowledge Remidio Innovative solutions for providing the fundus on phone (FOP) camera for conducting the study and Dr.  ... 
doi:10.1371/journal.pone.0138285 pmid:26401839 pmcid:PMC4581835 fatcat:aymkf5xnvvdfvdvdpfgfojj35m

Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Fabrice Meriaudeau
2018 Data  
This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.  ...  The dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image.  ...  in Signal and Image Processing research lab.  ... 
doi:10.3390/data3030025 fatcat:6keyluykszf33hd6nujbvhub4m

Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review

Beau J. Fenner, Raymond L. M. Wong, Wai-Ching Lam, Gavin S. W. Tan, Gemmy C. M. Cheung
2018 Ophthalmology and Therapy  
Rising prevalence of diabetes worldwide has necessitated the implementation of population-based diabetic retinopathy (DR) screening programs that can perform retinal imaging and interpretation for extremely  ...  Smartphone-based fundus photography, macular optical coherence tomography, ultrawide-field imaging, and artificial intelligence-based image reading address limitations of current approaches and will likely  ...  No funding or sponsorship was received for this study or publication of this article.  ... 
doi:10.1007/s40123-018-0153-7 pmid:30415454 pmcid:PMC6258577 fatcat:5nxdlalenvhexl4bvvigd2p3tq

Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

Sundaram Natarajan, Astha Jain, Radhika Krishnan, Ashwini Rogye, Sobha Sivaprasad
2019 JAMA ophthalmology  
Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial  ...  To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken  ...  Smartphone-based retinal imaging is emerging as a cost-effective way of screening for retinopathy in the community. 4, 5 Similarly, automated analysis of retinal images captured using standard retinal  ... 
doi:10.1001/jamaophthalmol.2019.2923 pmid:31393538 pmcid:PMC6692680 fatcat:ab6zgxfmnzefleg24ttdv45xri

Retinal Imaging Techniques for Diabetic Retinopathy Screening

James Kang Hao Goh, Carol Y. Cheung, Shaun Sebastian Sim, Pok Chien Tan, Gavin Siew Wei Tan, Tien Yin Wong
2016 Journal of Diabetes Science and Technology  
Due to the increasing prevalence of diabetes mellitus, demand for diabetic retinopathy (DR) screening platforms is steeply increasing.  ...  Hence, there has been a recent proliferation of automated retinal image analysis software that may potentially alleviate this burden cost-effectively.  ...  Automated retinal image analysis for diabetic retinopathy in telemedicine. Curr Diab Rep. 2015;15(3):14. 2003;38(7):557-568. 32.  ... 
doi:10.1177/1932296816629491 pmid:26830491 pmcid:PMC4773981 fatcat:sdt22dt3ovdmhngvaj45kh2aqe

Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations

Guanghua Zhang, Bin Sun, Zhixian Chen, Yuxi Gao, Zhaoxia Zhang, Keran Li, Weihua Yang
2022 Frontiers in Medicine  
that the proposed DGCN provides an innovative route for automated diabetic retinopathy grading and other computer-aided diagnostic systems.  ...  Three designed loss functions of graph-center, pseudo-contrastive, and transformation-invariant constrain the optimisation and application of the DGCN model in an automated diabetic retinopathy grading  ...  All images in the clinical validation sets were graded by several ophthalmologists for the presence of diabetic retinopathy.  ... 
doi:10.3389/fmed.2022.872214 pmid:35492360 pmcid:PMC9046841 fatcat:hwy6hqtq5zep3omaruisa5cani

Verification of 3D-printed universal smartphone retinal imaging adapter against conventional fundus camera imaging for diabetic retinopathy screening

Yuen Keat Gan, Amir Samsudin
2020 Malaysian journal of ophthalmology  
Screening for diabetic retinopathy (DR) is critical in preventing visual loss.  ...  The images were graded according to Early Treatment of Diabetic Retinopathy Study (ETDRS) classification. Agreement between both modalities was calculated using Cohen's Kappa statistics.  ...  Conclusion There was substantial agreement in grading DR severity between SRIA and conventional fundus camera imaging.  ... 
doi:10.35119/myjo.v2i3.124 fatcat:eduuvwoz3fh5diiza72lqn5nya

Assessment of diabetic retinopathy using two ultra-wide-field fundus imaging systems, the Clarus® and Optos™ systems

Takao Hirano, Akira Imai, Hirotsugu Kasamatsu, Shinji Kakihara, Yuichi Toriyama, Toshinori Murata
2018 BMC Ophthalmology  
The ability to image wide fundus fields and to conduct swift, non-invasive examinations is increasingly important with the escalation in patients with diabetic retinopathy (DR).  ...  Methods: Fifty eyes of 28 consecutive patients with DR were examined in this prospective observational study.  ...  HK, SK and YT worked on the data collection, analysis and manuscript writing. All authors read and approved the final manuscript.  ... 
doi:10.1186/s12886-018-1011-z fatcat:rtcsdwuy3zh2hogkytiegaqeoq

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

Valentina Bellemo, Zhan W Lim, Gilbert Lim, Quang D Nguyen, Yuchen Xie, Michelle Y T Yip, Haslina Hamzah, Jinyi Ho, Xin Q Lee, Wynne Hsu, Mong L Lee, Lillian Musonda (+8 others)
2019 The Lancet Digital Health  
A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited.  ...  fundus images.  ...  retinal fundus images.  ... 
doi:10.1016/s2589-7500(19)30004-4 pmid:33323239 fatcat:b7epasxgbfdyfoveocnxxxqcoa

Nonmydriatic digital retinal images for determining diabetic retinopathy

Jutalai Tanterdtham, Apichart Singalavanija, Chakrapong Namatra, Adisak Trinavarat, Nuttawut Rodanant, Parapun Bamroongsuk, Somanus Thoongsuwan, Wanna Euasobhon
2007 Journal of the Medical Association of Thailand = Chotmaihet thangphaet  
To evaluate the efficacy of nonmydriatic digital retinal images for determining diabetic retinopathy.  ...  International clinical diabetic retinopathy disease severity scale was used for grading diabetic retinopathy in all cases. Presence of diabetic retinopathy was detected in 70 eyes (31.1%).  ...  Sutipol Udompunturak, statistician of the Clinical Epidemiology Unit, office of Research Institute, Siriraj Hospital for the statistical analysis.  ... 
pmid:17427528 fatcat:fnvu37x6z5elbjmmlvjspgymbq

Discriminative convolution neural network architecture for diagnosis of diabetic retinopathy through classification and progression prediction of lesions grading in color fundus images of retina

R. Senthilkumar, A. Bharathi, S. Daniel Madan Raja
2022 International Journal of Health Sciences  
towards diagnosis of diabetic retinopathy through classification and progression prediction of lesion grading in fundus image of Retina.  ...  Machine learning models are less impressive for several staging diseases due to clinical grading.  ...  Pathology and anatomy analysis of the fundus image are closely associated with diabetic retinopathy and the presence of each of the aforementioned anomaly determines the grade of diabetic retinopathy such  ... 
doi:10.53730/ijhs.v6ns2.7731 fatcat:feb2uxc6kbbzpmwf7kfo6fc4zi

SDOCT Imaging to Identify Macular Pathology in Patients Diagnosed with Diabetic Maculopathy by a Digital Photographic Retinal Screening Programme

Sarah Mackenzie, Christian Schmermer, Amanda Charnley, Dawn Sim, Vikas Tah, Martin Dumskyj, Stephen Nussey, Catherine Egan, Landon Myer
2011 PLoS ONE  
Discussion: This analysis shows that patients with diabetes, mild to moderate non-proliferative diabetic retinopathy (R1) and evidence of diabetic maculopathy on non-stereoscopic retinal photographs (M1  ...  SDOCT imaging is a useful adjunct to colour fundus photography in screening for referable diabetic maculopathy in our screening population.  ...  Patients with proliferative diabetic retinopathy, severe non-proliferative retinopathy or un-gradeable retinal images in either eye were excluded from this analysis and were referred in the usual way to  ... 
doi:10.1371/journal.pone.0014811 pmid:21573106 pmcid:PMC3089611 fatcat:tag6qgaddfau7nduqtnoimlh6u

Analyzing fundus images to detect diabetic retinopathy (DR) using deep learning system in the Yangtze River delta region of China

Li Lu, Peifang Ren, Qianyi Lu, Enliang Zhou, Wangshu Yu, Jiani Huang, Xiaoying He, Wei Han
2021 Annals of Translational Medicine  
A DLS with a convolutional neural network was developed to recognize fundus images of referable diabetic retinopathy.  ...  This study aimed to establish and evaluate an artificial intelligence-based deep learning system (DLS) for automatic detection of diabetic retinopathy.  ...  ., Ltd. for providing technical support.  ... 
doi:10.21037/atm-20-3275 pmid:33708853 pmcid:PMC7940941 fatcat:szfzaku6yjfcne7tjin4ify7mu

Ultra-wide-field fundus photography: can it replace Early Treatment Diabetic Retinopathy Study 7 field photography?

Nazimul Hussain
2019 Annals of Eye Science  
Whether the peripheral retinal lesion in diabetic retinopathy could exists in addition to standard 7 field image lesions, hence enhancing the severity level of diabetic retinopathy.  ...  Besides, how peripheral DR changes can affect the evaluation of DR severity in contrast to ETDRS images. In image acquisition, authors utilized only the central UWF images for grading.  ...  Footnote Conflicts of Interest: The author has no conflicts of interest to declare.  ... 
doi:10.21037/aes.2019.01.03 fatcat:dwxdom5s5vhlpehfrhloxhl7pi
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