Development and Validation of a Cloud-based Deep Learning Platform for Detection of 37 Fundus Diseases in Retinal Photographs
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香港中文大學研究人員

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摘要Purpose
To develop and apply a cloud-based deep learning platform (DLP) for automated detection of 37 types of ocular fundus diseases and conditions in retinal photographs.

Methods
A DLP composed of various convolutional neural networks (CNNs) was constructed. After being trained with 96 758 fundus images, the diagnostic performance of DLP was validated with three datasets using 110 738 images for detection of 37 types of fundus diseases and conditions. Confusion matrix, overall accuracy (OA), Cohen’s Kappa and relative classifier information (RCI) were applied to evaluate the DLP performance of multi-category classification. Sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were applied for binary classification. All these results of the DLP were generated based on the reference standard of licensed ophthalmologists.

Results
The primary validation dataset consisted of 41 468 images from 32 614 subjects (mean age, 49.9 years, 51.2% men). The multihospital test sets had 58 251 images from 25 515 subjects (mean age, 55.0 years, 49.3% men). The screening categorized dataset had 11 019 images from 2594 subjects (mean age, 61.6 years, 27.8% men). For Classification of 29 categories, the DLP had an OA of 97.9%, a Kappa of 0.968 and an RCI of 0.939 for primary validation. For multihospital tests, OA range was 95.6% to 97.5%. For screening categorized, OA was 97.0%. For detecting referable in the “nonreferable-vs-referable” classification, AUC range was 0.999 (95%CI, 0.999-1.000), sensitivity was 99.4% (95% CI, 99.3%-99.5%) and specificity was 98.6% (95% CI, 98.5%-98.8%) for primary validation. For multihospital tests, AUC range was 0.996 to 0.999. For screening categorized, AUC was 0.995 (95%CI, 0.994-0.996). For classification of subcategories, accuracy range was 85.5% to 97.8%, AUC range 0.868 to 0.996. The DLP has been applied to community screening of retinal diseases and opened online for public use.

Conclusions
We have developed a cloud-based DLP for automated detection of 37 types of ocular fundus diseases and conditions with high OA, Kappa, RCI as well as high sensitivity, specificity and AUC.
Layman Abstract (optional): Provide a 50-200 word description of your work that non-scientists can understand. Describe the big picture and the implications of your findings, not the study itself and the associated details.
Millions of people in the world are affected by ocular fundus diseases. Without accurate diagnosis and appropriate treatments timely, these fundus diseases can lead to irreversible visual impairment or even blindness. Early detection and appropriate treatment of these diseases are important to prevent the vision loss. We have developed a cloud-based deep learning platform (DLP) capable of detecting all common referable retinal diseases and conditions. This study provided an important and significant insight and advancement for the AI research in ophthalmology. It could also provide a logic for diagnosis of all kinds of diseases in a field using one single examination by integrating the concepts of “disease” and “condition”. Our DLP could be feasible to be applied in health care settings to assist clinical diagnosis and community screening for retinal diseases.
著者Cen L, Ji J, Lin J, Pang CC, Zhang M
會議名稱The Annual Meeting of the Association for Research in Vision and Ophthalmology (ARVO) 2019
會議開始日28.04.2019
會議完結日02.05.2019
會議地點Vancouver, British Columbia
會議國家/地區加拿大
會議論文集題名INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE
出版年份2019
卷號60
期次9
國際標準期刊號0146-0404
語言英式英語

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