Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images.
Nat Biomed Eng
; 5(6): 533-545, 2021 06.
Article
en En
| MEDLINE
| ID: mdl-34131321
ABSTRACT
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 m2 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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Retina
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Interpretación de Imagen Asistida por Computador
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Fotograbar
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Diabetes Mellitus Tipo 2
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Insuficiencia Renal Crónica
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Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Screening_studies
Límite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Nat Biomed Eng
Año:
2021
Tipo del documento:
Article
País de afiliación:
China