June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Bi-Modal Deep Learning for Recognizing Multiple Retinal Diseases based on Color Fundus Photos and OCT Images
Author Affiliations & Notes
  • Jingyuan Yang
    Ophthalmology, Peking Union Medical College Hospital, Beijing, China
    Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
  • Zhou Yang
    Vistel AI Lab, Visionary Intelligence Ltd., China
  • Zaixing Mao
    Topcon Medical Systems, Japan
  • jianchun zhao
    Vistel AI Lab, Visionary Intelligence Ltd., China
  • Bing Li
    Ophthalmology, Peking Union Medical College Hospital, Beijing, China
    Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
  • Bilei Zhang
    Ophthalmology, Peking Union Medical College Hospital, Beijing, China
    Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
  • Yuelin Wang
    Ophthalmology, Peking Union Medical College Hospital, Beijing, China
    Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
  • Hailan Lin
    Key Lab of DEKE, Renmin University of China, Beijing, China
  • Jie Wang
    Key Lab of DEKE, Renmin University of China, Beijing, China
  • Jonathan Liu
    Topcon Medical Systems, Japan
  • Shuyun Yeh
    Topcon Medical Systems, Japan
  • Yasufumi Fukuma
    Topcon Medical Systems, Japan
  • Dayong Ding
    Vistel AI Lab, Visionary Intelligence Ltd., China
  • Xirong Li
    Key Lab of DEKE, Renmin University of China, Beijing, China
  • Weihong Yu
    Ophthalmology, Peking Union Medical College Hospital, Beijing, China
    Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
  • Youxin Chen
    Ophthalmology, Peking Union Medical College Hospital, Beijing, China
    Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing, China
  • Footnotes
    Commercial Relationships   Jingyuan Yang, None; Zhou Yang, None; Zaixing Mao, None; jianchun zhao, None; Bing Li, None; Bilei Zhang, None; Yuelin Wang, None; Hailan Lin, None; Jie Wang, None; Jonathan Liu, None; Shuyun Yeh, None; Yasufumi Fukuma, None; Dayong Ding, None; Xirong Li, None; Weihong Yu, None; Youxin Chen, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2107. doi:
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    • Get Citation

      Jingyuan Yang, Zhou Yang, Zaixing Mao, jianchun zhao, Bing Li, Bilei Zhang, Yuelin Wang, Hailan Lin, Jie Wang, Jonathan Liu, Shuyun Yeh, Yasufumi Fukuma, Dayong Ding, Xirong Li, Weihong Yu, Youxin Chen; Bi-Modal Deep Learning for Recognizing Multiple Retinal Diseases based on Color Fundus Photos and OCT Images. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2107.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Artificial intelligence (AI)-based diagnosis of retinal diseases using a single imaging method of color fundus photography (FP) or optical coherence tomography (OCT) has been fully investigated.
Although bi-modal imaging examinations using both FP and OCT could provide more comprehensive retinal information than their single-modal counterparts, which might improve the accuracy of AI to detect retinal diseases, the feasibility and performance of bi-modal imaging-based AI diagnosis of retinal diseases have not been extensively investigated.
The purpose of this study is to evaluate the performance of a bi-modal imaging-based AI system in detecting multiple retinal diseases using both FP and OCT images.

Methods : The AI-based system was trained using 573 scans (from 400 patients) centered on macula, validated using 192 scans (from 140 patients), and tested using 209 scans (from another 146 patients). Each scan that captured with Topcon 3D OCT-1 Maestro (Topcon Corp., Japan) consists of one FP and one corresponding radial OCT scan, which further consists of 12 OCT B-scans. Using both images and additional summarized clinical data, at least two ophthalmologists confirmed the diagnosis of each scan, including diabetic retinopathy (DR), dry age-related macular degeneration (AMD), wet AMD, epiretinal membrane (ERM), pathologic myopia (PM), macular oedema (ME), and normal (Fig 1). Performance of this system was measured in terms of sensitivity and specificity, and average precision (AP) per diagnosis.

Results : The bi-modal image-based AI system had a mean AP for the detection of multiple retinal diseases of 0.84 (95% confidence interval [CI], 0.80 - 0.87), a sensitivity of 0.88 (95% CI, 0.84-0.93), and a specificity of 0.91 (95% CI, 0.88-0.95) (Fig 2). Compared with the bi-modal image-based AI system, the mean AP was 0.74 when the AI system was trained with only FP images (95% CI 0.69-0.78), and 0.81 when with only OCT images (95% CI, 0.77-0.84) (Fig 2).

Conclusions : AI-assisted diagnostic system based on bi-modal imaging method showed significantly better performance than that based on FP, and relatively better performance than that based on OCT. The additional information provided by bi-modal imaging method could be used for not only diagnosis, but also perhaps therapeutic decision, which needs further study.

This is a 2021 ARVO Annual Meeting abstract.

 

Fig 1. Schematic diagram of the present study.

Fig 1. Schematic diagram of the present study.

 

Fig 2. AP per diagnosis.

Fig 2. AP per diagnosis.

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