A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study.
J Diabetes
; 14(2): 111-120, 2022 Feb.
Article
in En
| MEDLINE
| ID: mdl-34889059
ABSTRACT
BACKGROUND:
The aim of our research was to prospectively explore the clinical value of a deep learning algorithm (DLA) to detect referable diabetic retinopathy (DR) in different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, glycosylated hemoglobin (HbA1c), diabetes duration, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) at a real-world diabetes center in China.METHODS:
A total of 1147 diabetic patients from Shanghai General Hospital were recruited from October 2018 to August 2019. Retinal fundus images were graded by the DLA, and the detection of referable DR (moderate nonproliferative DR or worse) was compared with a reference standard generated by one certified retinal specialist with more than 12 years of experience. The performance of DLA across different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, HbA1c, diabetes duration, UACR, and eGFR was evaluated.RESULTS:
For all 1674 gradable images, the area under the receiver operating curve, sensitivity, and specificity of the DLA for referable DR were 0.942 (95% CI, 0.920-0.964), 85.1% (95% CI, 83.4%-86.8%), and 95.6% (95% CI, 94.6%-96.6%), respectively. The DLA showed consistent performance across most subgroups, while it showed superior performance in the subgroups of patients with type 1 diabetes, UACR ≥ 30 mg/g, and eGFR < 90 mL/min/1.73m2 .CONCLUSIONS:
This study showed that the DLA was a reliable alternative method for the detection of referable DR and performed superior in patients with type 1 diabetes and diabetic nephropathy who were prone to DR.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Diabetes Mellitus
/
Diabetic Retinopathy
/
Deep Learning
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Humans
Country/Region as subject:
Asia
Language:
En
Journal:
J Diabetes
Journal subject:
ENDOCRINOLOGIA
Year:
2022
Document type:
Article
Affiliation country:
China
Publication country:
AU
/
AUSTRALIA
/
AUSTRÁLIA