July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Fundus Photograph-based Deep Learning for Estimation of Blood Bilirubin in a Chinese Population
Author Affiliations & Notes
  • Hua Wang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Hefei Innovation Research Institute, Beihang University, HeFei, Anhui Province, China
  • Jicong Zhang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, Beijing, China
  • Yang Yu
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Hefei Innovation Research Institute, Beihang University, HeFei, Anhui Province, China
  • Kecai Lu
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Hefei Innovation Research Institute, Beihang University, HeFei, Anhui Province, China
  • Yanchao Yuan
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Hefei Innovation Research Institute, Beihang University, HeFei, Anhui Province, China
  • Xiong Wang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Hefei Innovation Research Institute, Beihang University, HeFei, Anhui Province, China
  • Mingkun Bao
    School of Biological Science and Medical Engineering, Beihang University, Beijing, Beijing, China
    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, Beijing, China
  • Shuohua Chen
    Health Care Center, Kailuan Group, Hebei, Tangshan, China
  • Shouling Wu
    Cardiology, Kailuan General Hospital, Tangshan, China
  • wenbin wei
    Department of Ophthalmology, Beijing Tongren Hospital, Beijing, Beijing, China
  • Yaxing Wang
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, Beijing, China
  • Jost B Jonas
    Ophthalmology, Medical Faculty Mannheim-Heidelberg, Germany
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, Beijing, China
  • Footnotes
    Commercial Relationships   Hua Wang, None; Jicong Zhang, None; Yang Yu, None; Kecai Lu, None; Yanchao Yuan, None; Xiong Wang, None; Mingkun Bao, None; Shuohua Chen, None; Shouling Wu, None; wenbin wei, None; Yaxing Wang, None; Jost Jonas, None
  • Footnotes
    Support  This work is supported by the National Key Research and Development Program of China (grant number 2016YFF0201002), funded by the Ministry of Science and Technology of China; the National Natural Science Foundation of China (grant numbers 61301005,61572055 and 81501155); the Hefei Innovation Research Institute, Beihang University; and the Thousand Young Talent Plan Station (to J.C.Z.) between Beihang University and Jiangsu Yuwell Medical Equipment and Supply Co. Ltd.
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1446. doi:
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    • Get Citation

      Hua Wang, Jicong Zhang, Yang Yu, Kecai Lu, Yanchao Yuan, Xiong Wang, Mingkun Bao, Shuohua Chen, Shouling Wu, wenbin wei, Yaxing Wang, Jost B Jonas; Fundus Photograph-based Deep Learning for Estimation of Blood Bilirubin in a Chinese Population. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1446.

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

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Abstract

Purpose : To propose a deep learning method to estimate the values of blood total bilirubin and direct bilirubinbased on fundus photography.

Methods : The Kailuan Study is a population-based prospective cohort study, with a total of 101,510 individuals aged 18-98 years recruited. Participants were followed and re-examined every 2 year since 2006-2007. All participants recruited underwent routine history and physical examinations, anthropometry, and laboratory assessment. Ophthalmic examinations were applied, including visual acuity, ocular biometry, fundus photography and optical coherence tomography, in a randomly selected subgroup composed of 14594 participants during 2015 to 2016. The bilirubinblood test was performed within 1 year of the time fundus image was taken (Table 1). A shift group model was applied to group the fundus photos within a specified range of bilirubin (0.5μmol/L), based on which a training set (70%) and a validation set (30%) with comparable distribution of imaging sort were established (Fig 2). A deep learning model of mixture of experts was used to explore the relationship between the bilirubin and the retina imaging (Figure 3).Performance of the model for discrimination of direct and total bilirubinwith fundus photography were characterized by coefficient of determination (R2), mean absolute error (MAE), and root mean absolute error (RMSE).

Results : Our mixture of experts model was able to estimate the value of both direct and total bilirubinfrom fundus photograph, with R2of 0.986 and 0.861, respectively (Table 2). The performance was also demonstrated by the confuse matrix (Figure 4).

Conclusions : With the proposed deep learning model, the bilirubin value can be accurately calculated from fundus photography independently in a large Chinese population. It suggested the association between the eye and the body, and the potential value of retina imaging for systematical disease discrimination in the future.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

 

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