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Automated Multidimensional Deep Learning Platform for Referable Diabetic Retinopathy Detection: a Multicentre, Retrospective Study by Guihua Zhang; Jian-Wei Lin; Ji Wang; Jie Ji; Ling-Ping Cen; Weiqi Chen; Peiwen Xie; Yi Zheng; Yongqun Xiong; Hanfu Wu; Dongjie Li; Tsz Kin Ng; Chi Pui Pang; Mingzhi Zhang is a Medicine article available to read on EtoBox.
What is Automated Multidimensional Deep Learning Platform for Referable Diabetic Retinopathy Detection: a Multicentre, Retrospective Study about?
## Objective To develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection. ## Design This is a multicentre platform development study based on retrospective, cross-sectional data sets. Images were labelled by two-level certificated graders as the ground truth. According to the UK DR screening guideline, a DL model based on colour retinal images with five-dimensional classifiers, namely image quality, retinopathy, maculopathy gradability, maculopathy and photocoagulation, was developed. Referable decisions were generated by integrating the output of all classifiers and reported at the image, eye and patient level. The performance of the DL was compared with DR experts. ## Setting DR screening programmes from three hospitals and the Lifeline Express Diabetic Retinopathy Screening Program in China. ## Participants 83 465 images of 39 836 eyes from 21 716 patients were annotated, of which 53 211 images were used as the development set and 30 254 images were used as the external validation set, split based on centre and period. ## Main outcomes Accuracy, F1 score, sensitivity, specificity, area under the rece
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- Author
- Guihua Zhang; Jian-Wei Lin; Ji Wang; Jie Ji; Ling-Ping Cen; Weiqi Chen; Peiwen Xie; Yi Zheng; Yongqun Xiong; Hanfu Wu; Dongjie Li; Tsz Kin Ng; Chi Pui Pang; Mingzhi Zhang
- Publisher
- BMJ
- Published
- 2022
- Language
- EN
- Field
- Medicine (Health Sciences)