TY - JOUR
T1 - Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk
AU - Nusinovici, Simon
AU - Rim, Tyler Hyungtaek
AU - Yu, Marco
AU - Lee, Geunyoung
AU - Tham, Yih Chung
AU - Cheung, Ning
AU - Chong, Crystal Chun Yuen
AU - Da Soh, Zhi
AU - Thakur, Sahil
AU - Lee, Chan Joo
AU - Sabanayagam, Charumathi
AU - Lee, Byoung Kwon
AU - Park, Sungha
AU - Kim, Sung Soo
AU - Kim, Hyeon Chang
AU - Wong, Tien Yin
AU - Cheng, Ching Yu
N1 - Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85127400302&partnerID=8YFLogxK
U2 - 10.1093/ageing/afac065
DO - 10.1093/ageing/afac065
M3 - Article
C2 - 35363255
AN - SCOPUS:85127400302
SN - 0002-0729
VL - 51
JO - Age and Ageing
JF - Age and Ageing
IS - 4
M1 - afac065
ER -
