Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk

Simon Nusinovici, Tyler Hyungtaek Rim, Marco Yu, Geunyoung Lee, Yih Chung Tham, Ning Cheung, Crystal Chun Yuen Chong, Zhi Da Soh, Sahil Thakur, Chan Joo Lee, Charumathi Sabanayagam, Byoung Kwon Lee, Sungha Park, Sung Soo Kim, Hyeon Chang Kim, Tien Yin Wong, Ching Yu Cheng

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Background: ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA). Objective: we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations. Methods: we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs). Results: in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42-1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69-3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31-1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14-1.69]) and 18% (HR = 1.18 [1.10-1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%). Conclusions: the DL-derived RetiAGE provides a novel, alternative approach to measure ageing.

Original languageEnglish
Article numberafac065
JournalAge and Ageing
Volume51
Issue number4
DOIs
Publication statusPublished - 2022 Apr 1

Bibliographical note

Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Ageing
  • Geriatrics and Gerontology

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