Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images.

Publisher: Springer Nature Country of Publication: England NLM ID: 101696896 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2157-846X (Electronic) Linking ISSN: 2157846X NLM ISO Abbreviation: Nat Biomed Eng Subsets: MEDLINE

Publication: London : Springer Nature
Original Publication: [London] : Macmillan Publishers Limited, [2016]-

Deep Learning*; Diabetes Mellitus, Type 2/*diagnostic imaging ; Image Interpretation, Computer-Assisted/*statistics & numerical data ; Photography/*statistics & numerical data ; Renal Insufficiency, Chronic/*diagnostic imaging ; Retina/*diagnostic imaging; Area Under Curve ; Blood Glucose/metabolism ; Body Height ; Body Mass Index ; Body Weight ; Diabetes Mellitus, Type 2/metabolism ; Diabetes Mellitus, Type 2/pathology ; Disease Progression ; Female ; Fundus Oculi ; Glomerular Filtration Rate ; Humans ; Male ; Metadata/statistics & numerical data ; Middle Aged ; Neural Networks, Computer ; Photography/methods ; Prospective Studies ; ROC Curve ; Renal Insufficiency, Chronic/metabolism ; Renal Insufficiency, Chronic/pathology ; Retina/metabolism ; Retina/pathology

Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min ^-1 per 1.73 m ² and 0.65-1.1 mmol l ^-1 , and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.

Comment in: Nat Biomed Eng. 2021 Jun;5(6):487-489. (PMID: 34131320)

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216593/Z/19/Z United Kingdom WT_ Wellcome Trust

0 (Blood Glucose)

Date Created: 20210616 Date Completed: 20210831 Latest Revision: 20211001

20231215

10.1038/s41551-021-00745-6

34131321