Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk
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 photograph...
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| Veröffentlicht in: | Age and ageing 2022-04, Vol.51 (4) |
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| creator | Nusinovici, Simon Rim, Tyler Hyungtaek Yu, Marco Lee, Geunyoung Tham, Yih-Chung Cheung, Ning Chong, Crystal Chun Yuen Da Soh, Zhi Thakur, Sahil Lee, Chan Joo Sabanayagam, Charumathi Lee, Byoung Kwon Park, Sungha Kim, Sung Soo Kim, Hyeon Chang Wong, Tien-Yin Cheng, Ching-Yu |
| description | 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. |
| doi_str_mv | 10.1093/ageing/afac065 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8973000</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/ageing/afac065</oup_id><sourcerecordid>2646718079</sourcerecordid><originalsourceid>FETCH-LOGICAL-c518t-8a8d451efdb40ff367f507d4417cd7b4da5ff41ac0a2ebef05364a2f62c2a2bf3</originalsourceid><addsrcrecordid>eNqFkc9rFTEQx4Mo9lm9epQFLxXcNskm2d2LUIq_oFAQPYfZZLIv7b7NmmSF_vemfc-iXnoahvnMd358CXnN6CmjfXMGI_p5PAMHhir5hGyYUF3Nu0Y8JRtKKa9py_sj8iKl65IyyfhzctTIRjVcyg25-YbZzzBVyzbkMEZYtvUACW1lEZdqQohzGVAtEa03OVWDD1MYvSktZfb7CmZbpRwhe-cxVbsQB299vr0vlCzDdJdFn25ekmcOpoSvDvGY_Pj08fvFl_ry6vPXi_PL2kjW5bqDzgrJ0NlBUOca1TpJWysEa41tB2FBOidYORg4DuhoOUYAd4obDnxwzTH5sNdd1mGH1uBc9pv0Ev0O4q0O4PW_ldlv9Rh-6a5vm_KkInByEIjh54op651PBqcJZgxr0lwJ1bKOtn1B3_6HXoc1lofeUy1ViveyUKd7ysSQUkT3sAyj-s5HvfdRH3wsDW_-PuEB_2NcAd7tgbAuj4n9BjE_rL4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2647066295</pqid></control><display><type>article</type><title>Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk</title><source>Applied Social Sciences Index & Abstracts (ASSIA)</source><source>Oxford University Press Journals</source><source>MEDLINE</source><source>Alma/SFX Local Collection</source><source>EZB Electronic Journals Library</source><creator>Nusinovici, Simon ; Rim, Tyler Hyungtaek ; Yu, Marco ; Lee, Geunyoung ; Tham, Yih-Chung ; Cheung, Ning ; Chong, Crystal Chun Yuen ; Da Soh, Zhi ; Thakur, Sahil ; Lee, Chan Joo ; Sabanayagam, Charumathi ; Lee, Byoung Kwon ; Park, Sungha ; Kim, Sung Soo ; Kim, Hyeon Chang ; Wong, Tien-Yin ; Cheng, Ching-Yu</creator><creatorcontrib>Nusinovici, Simon ; Rim, Tyler Hyungtaek ; Yu, Marco ; Lee, Geunyoung ; Tham, Yih-Chung ; Cheung, Ning ; Chong, Crystal Chun Yuen ; Da Soh, Zhi ; Thakur, Sahil ; Lee, Chan Joo ; Sabanayagam, Charumathi ; Lee, Byoung Kwon ; Park, Sungha ; Kim, Sung Soo ; Kim, Hyeon Chang ; Wong, Tien-Yin ; Cheng, Ching-Yu</creatorcontrib><description>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.</description><identifier>ISSN: 0002-0729</identifier><identifier>EISSN: 1468-2834</identifier><identifier>DOI: 10.1093/ageing/afac065</identifier><identifier>PMID: 35363255</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Ability ; Aged ; Aging ; Aging - physiology ; Algorithms ; Alternative approaches ; Artificial intelligence ; Biobanks ; Biological markers ; Biomarkers ; Cancer ; Cardiovascular diseases ; Deep Learning ; Discrimination ; Humans ; Learning ; Medical screening ; Morbidity ; Mortality ; Photography ; Proportional Hazards Models ; Research Paper ; Retina ; Risk Factors ; Stratification</subject><ispartof>Age and ageing, 2022-04, Vol.51 (4)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c518t-8a8d451efdb40ff367f507d4417cd7b4da5ff41ac0a2ebef05364a2f62c2a2bf3</citedby><cites>FETCH-LOGICAL-c518t-8a8d451efdb40ff367f507d4417cd7b4da5ff41ac0a2ebef05364a2f62c2a2bf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902,30976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35363255$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nusinovici, Simon</creatorcontrib><creatorcontrib>Rim, Tyler Hyungtaek</creatorcontrib><creatorcontrib>Yu, Marco</creatorcontrib><creatorcontrib>Lee, Geunyoung</creatorcontrib><creatorcontrib>Tham, Yih-Chung</creatorcontrib><creatorcontrib>Cheung, Ning</creatorcontrib><creatorcontrib>Chong, Crystal Chun Yuen</creatorcontrib><creatorcontrib>Da Soh, Zhi</creatorcontrib><creatorcontrib>Thakur, Sahil</creatorcontrib><creatorcontrib>Lee, Chan Joo</creatorcontrib><creatorcontrib>Sabanayagam, Charumathi</creatorcontrib><creatorcontrib>Lee, Byoung Kwon</creatorcontrib><creatorcontrib>Park, Sungha</creatorcontrib><creatorcontrib>Kim, Sung Soo</creatorcontrib><creatorcontrib>Kim, Hyeon Chang</creatorcontrib><creatorcontrib>Wong, Tien-Yin</creatorcontrib><creatorcontrib>Cheng, Ching-Yu</creatorcontrib><title>Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk</title><title>Age and ageing</title><addtitle>Age Ageing</addtitle><description>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.</description><subject>Ability</subject><subject>Aged</subject><subject>Aging</subject><subject>Aging - physiology</subject><subject>Algorithms</subject><subject>Alternative approaches</subject><subject>Artificial intelligence</subject><subject>Biobanks</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Cardiovascular diseases</subject><subject>Deep Learning</subject><subject>Discrimination</subject><subject>Humans</subject><subject>Learning</subject><subject>Medical screening</subject><subject>Morbidity</subject><subject>Mortality</subject><subject>Photography</subject><subject>Proportional Hazards Models</subject><subject>Research Paper</subject><subject>Retina</subject><subject>Risk Factors</subject><subject>Stratification</subject><issn>0002-0729</issn><issn>1468-2834</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><recordid>eNqFkc9rFTEQx4Mo9lm9epQFLxXcNskm2d2LUIq_oFAQPYfZZLIv7b7NmmSF_vemfc-iXnoahvnMd358CXnN6CmjfXMGI_p5PAMHhir5hGyYUF3Nu0Y8JRtKKa9py_sj8iKl65IyyfhzctTIRjVcyg25-YbZzzBVyzbkMEZYtvUACW1lEZdqQohzGVAtEa03OVWDD1MYvSktZfb7CmZbpRwhe-cxVbsQB299vr0vlCzDdJdFn25ekmcOpoSvDvGY_Pj08fvFl_ry6vPXi_PL2kjW5bqDzgrJ0NlBUOca1TpJWysEa41tB2FBOidYORg4DuhoOUYAd4obDnxwzTH5sNdd1mGH1uBc9pv0Ev0O4q0O4PW_ldlv9Rh-6a5vm_KkInByEIjh54op651PBqcJZgxr0lwJ1bKOtn1B3_6HXoc1lofeUy1ViveyUKd7ysSQUkT3sAyj-s5HvfdRH3wsDW_-PuEB_2NcAd7tgbAuj4n9BjE_rL4</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Nusinovici, Simon</creator><creator>Rim, Tyler Hyungtaek</creator><creator>Yu, Marco</creator><creator>Lee, Geunyoung</creator><creator>Tham, Yih-Chung</creator><creator>Cheung, Ning</creator><creator>Chong, Crystal Chun Yuen</creator><creator>Da Soh, Zhi</creator><creator>Thakur, Sahil</creator><creator>Lee, Chan Joo</creator><creator>Sabanayagam, Charumathi</creator><creator>Lee, Byoung Kwon</creator><creator>Park, Sungha</creator><creator>Kim, Sung Soo</creator><creator>Kim, Hyeon Chang</creator><creator>Wong, Tien-Yin</creator><creator>Cheng, Ching-Yu</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QJ</scope><scope>7T5</scope><scope>7TK</scope><scope>7U9</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220401</creationdate><title>Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk</title><author>Nusinovici, Simon ; Rim, Tyler Hyungtaek ; Yu, Marco ; Lee, Geunyoung ; Tham, Yih-Chung ; Cheung, Ning ; Chong, Crystal Chun Yuen ; Da Soh, Zhi ; Thakur, Sahil ; Lee, Chan Joo ; Sabanayagam, Charumathi ; Lee, Byoung Kwon ; Park, Sungha ; Kim, Sung Soo ; Kim, Hyeon Chang ; Wong, Tien-Yin ; Cheng, Ching-Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c518t-8a8d451efdb40ff367f507d4417cd7b4da5ff41ac0a2ebef05364a2f62c2a2bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Ability</topic><topic>Aged</topic><topic>Aging</topic><topic>Aging - physiology</topic><topic>Algorithms</topic><topic>Alternative approaches</topic><topic>Artificial intelligence</topic><topic>Biobanks</topic><topic>Biological markers</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Cardiovascular diseases</topic><topic>Deep Learning</topic><topic>Discrimination</topic><topic>Humans</topic><topic>Learning</topic><topic>Medical screening</topic><topic>Morbidity</topic><topic>Mortality</topic><topic>Photography</topic><topic>Proportional Hazards Models</topic><topic>Research Paper</topic><topic>Retina</topic><topic>Risk Factors</topic><topic>Stratification</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nusinovici, Simon</creatorcontrib><creatorcontrib>Rim, Tyler Hyungtaek</creatorcontrib><creatorcontrib>Yu, Marco</creatorcontrib><creatorcontrib>Lee, Geunyoung</creatorcontrib><creatorcontrib>Tham, Yih-Chung</creatorcontrib><creatorcontrib>Cheung, Ning</creatorcontrib><creatorcontrib>Chong, Crystal Chun Yuen</creatorcontrib><creatorcontrib>Da Soh, Zhi</creatorcontrib><creatorcontrib>Thakur, Sahil</creatorcontrib><creatorcontrib>Lee, Chan Joo</creatorcontrib><creatorcontrib>Sabanayagam, Charumathi</creatorcontrib><creatorcontrib>Lee, Byoung Kwon</creatorcontrib><creatorcontrib>Park, Sungha</creatorcontrib><creatorcontrib>Kim, Sung Soo</creatorcontrib><creatorcontrib>Kim, Hyeon Chang</creatorcontrib><creatorcontrib>Wong, Tien-Yin</creatorcontrib><creatorcontrib>Cheng, Ching-Yu</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Age and ageing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nusinovici, Simon</au><au>Rim, Tyler Hyungtaek</au><au>Yu, Marco</au><au>Lee, Geunyoung</au><au>Tham, Yih-Chung</au><au>Cheung, Ning</au><au>Chong, Crystal Chun Yuen</au><au>Da Soh, Zhi</au><au>Thakur, Sahil</au><au>Lee, Chan Joo</au><au>Sabanayagam, Charumathi</au><au>Lee, Byoung Kwon</au><au>Park, Sungha</au><au>Kim, Sung Soo</au><au>Kim, Hyeon Chang</au><au>Wong, Tien-Yin</au><au>Cheng, Ching-Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk</atitle><jtitle>Age and ageing</jtitle><addtitle>Age Ageing</addtitle><date>2022-04-01</date><risdate>2022</risdate><volume>51</volume><issue>4</issue><issn>0002-0729</issn><eissn>1468-2834</eissn><abstract>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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35363255</pmid><doi>10.1093/ageing/afac065</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Ability Aged Aging Aging - physiology Algorithms Alternative approaches Artificial intelligence Biobanks Biological markers Biomarkers Cancer Cardiovascular diseases Deep Learning Discrimination Humans Learning Medical screening Morbidity Mortality Photography Proportional Hazards Models Research Paper Retina Risk Factors Stratification |
| title | Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk |
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