2020
DOI: 10.2196/23472
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Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

Abstract: Background Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning–based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning–based method using retinal images for detecting early renal function impairment has not yet been well studied. Objective This study aimed to develop and evaluate a deep learning model for detecting ear… Show more

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Cited by 26 publications
(30 citation statements)
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References 32 publications
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“…The efficacy during application to a real-world clinical setting may be affected by the patient’s condition and the image quality [ 39 ]. Additionally, some ocular diseases affecting image signal transmission could affect image quality and retinal disease diagnosis [ 40 , 41 ]. Third, images from different machine manufacturers not included in our study might have affected the model accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…The efficacy during application to a real-world clinical setting may be affected by the patient’s condition and the image quality [ 39 ]. Additionally, some ocular diseases affecting image signal transmission could affect image quality and retinal disease diagnosis [ 40 , 41 ]. Third, images from different machine manufacturers not included in our study might have affected the model accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…This lacked external validation ( 80 ). Kang et al ( 28 ) sought to predict early renal impairment from RFP, defined as eGFR < 90 ml/min/1.73 m 2 , but observed poor specificity. They noted false positives arising from RFP with retinal scarring, subretinal fluid, or optic disc swelling.…”
Section: Retinal Fundus Photographymentioning
confidence: 99%
“…Hence, clinical utility might be limited as many concomitant ophthalmic pathologies can cause such retinal structural manifestations. Features used to identify CKD or predict eGFR are unclear—saliency maps ( 28 , 39 ) have highlighted changes in retinal vasculature (dilatation of venules, rarefaction of vessels) and abnormal lesions characteristic of retinopathy (hemorrhages and exudations). A model by Rim et al ( 38 ) showed moderate performance in predicting creatinine levels ( R 2 : 0.38) when trained and tested on a South Korean dataset but was unable to generalize to a European dataset (UK Biobank, R 2 : 0.01).…”
Section: Retinal Fundus Photographymentioning
confidence: 99%
“…The fields of application range from classification [ 19 , 20 , 21 ] to prediction [ 22 , 23 ]. Furthermore, generative adversarial networks (GANs) have been used for efficient data augmentation and to solve data privacy concerns [ 24 , 25 ].…”
Section: Introductionmentioning
confidence: 99%