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A deep learning system for the detection of optic disc neovascularization in diabetic retinopathy using optical coherence tomography angiography images

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Abstract

As one of the major complications of diabetic retinopathy (DR), neovascularization of the optic disc (NVD) is a leading cause of visual impairment and blindness. Early identification and timely treatment of NVD are essential to prevent these risks. In this paper, we develop a deep learning (DL) system to identify, quantify, and visualize NVD from optical coherence tomography angiography (OCTA) images. Two datasets of OCTA images were used in this study to develop and evaluate the DL system: (1) 24,576 OCTA images collected from 96 patients with NVD; (2) 15,360 OCTA images from 60 NVD patients with NVD. The task of the DL system involved the detection of the optic disc boundary, the identification of the NVD regions, and the construction and calculation of 3D images for these regions. The DL system achieved promising results in the detection of the optic disc boundary and the identification of NVD regions. The accuracy of the DL system was significantly better than other DL algorithms and comparable to the performance of retina specialists. Furthermore, the DL system could provide a more intuitive 3D image for visualizing the NVD and its blood flow information.

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Data availability

The data generated and analyzed in this study are not available to the general public, as they are also used in an ongoing research project. However, they can be obtained on request from the corresponding author.

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Funding

Funding for this study was provided by the College-level Project Fund of Shanghai Sixth People’s Hospital (Grant No. ynlc201909) and the Interdisciplinary Program of Shanghai Jiao Tong University (Project No.YG2022QN089). This work was supported in part by the Clinical Special Program of Shanghai Municipal Health Commission (20224044), the Chronic disease health management and comprehensive intervention based on big data application (GWVI-8) and the Research on health management strategy and application of elderly population (GWVI-11.1–28).

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Author notes

  1. Xiang-ning Wang, Zhouyu Guan and Bo Qian contributed equally to this work and shared the co-first authorship.

Authors and Affiliations

  1. Department of Ophthalmology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Xiang-ning Wang & Qiang Wu

  2. Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Wuxi, Shanghai, China

    Zhouyu Guan

  3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Bo Qian

  4. Huadong Sanatorium, Wuxi, Jiangsu, China

    Tingli Chen

Authors
  1. Xiang-ning Wang
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  2. Zhouyu Guan
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  4. Tingli Chen
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  5. Qiang Wu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XW, ZG, TC and BQ. The first draft of the manuscript was written by XW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qiang Wu.

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Wang, Xn., Guan, Z., Qian, B. et al. A deep learning system for the detection of optic disc neovascularization in diabetic retinopathy using optical coherence tomography angiography images. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03418-y

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