Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

Ahmed Fetit; Siyamalan Manivannan; Sarah McGrory; Lucia Ballerini; Alexander Doney; Thomas J. MacGillivray; Ian J. Deary; Joanna M. Wardlaw; Fergus Doubal; Gareth J. McKay; Stephen McKenna; Emanuele Trucco

doi:10.1007/978-3-319-67561-9_17

Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

Ahmed Fetit, Siyamalan Manivannan, Sarah McGrory, Lucia Ballerini, Alexander Doney, Thomas J. MacGillivray, Ian J. Deary, Joanna M. Wardlaw, Fergus Doubal, Gareth J. McKay, Stephen McKenna, Emanuele Trucco

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Dementia is a devastating disease, and has severe implications on affected individuals, their family and wider society. A growing body of literature is studying the association of retinal microvasculature measurement with dementia. We present a pilot study testing the strength of groups of conventional (semantic) and texture-based (non-semantic) measurements extracted from retinal fundus camera images to classify patients with and without dementia. We performed a 500-trial bootstrap analysis with regularized logistic regression on a cohort of 1,742 elderly diabetic individuals (median age 72.2). Age was the strongest predictor for this elderly cohort. Semantic retinal measurements featured in up to 81% of the bootstrap trials, with arterial caliber and optic disk size chosen most often, suggesting that they do complement age when selected together in a classifier. Textural features were able to train classifiers that match the performance of age, suggesting they are potentially a rich source of information for dementia outcome classification

Original language	English
Title of host publication	Fetal, Infant and Ophthalmic Medical Image Analysis
Subtitle of host publication	International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI2017, Quebec City, QC, Canada, September 14, Proceedings
Editors	J. Cardoso, T. Arbel, A. Melbourne, H. Bogunovic, P. Moeskops, X. Chen, E. Schwartz, M. Garvin, E. Robinson, E. Trucco, M. Ebner, Y. Xu, A. Makropoulos, A. Desjardin, T. Vercauteren
Place of Publication	Switzerland
Publisher	Springer
Pages	150-158
Number of pages	9
ISBN (Electronic)	9783319675619
ISBN (Print)	9783319675602
DOIs	https://doi.org/10.1007/978-3-319-67561-9_17 https://doi.org/10.1007/978-3-319-67561-9
Publication status	Published - 14 Sept 2017
Event	4th MICCAI Workshop on Ophthalmic Medical Image Analysis - Quebec, Canada Duration: 10 Sept 2017 → 14 Sept 2017 https://sites.google.com/site/mwomia4/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	10554
ISSN (Print)	0302-9743

Conference

Conference	4th MICCAI Workshop on Ophthalmic Medical Image Analysis
Country/Territory	Canada
City	Quebec
Period	10/09/17 → 14/09/17
Internet address	https://sites.google.com/site/mwomia4/

Keywords

Retina
Dementia
Microvasculature
Classification
Biomarkers

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Author Accepted Manuscript
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-67561-9
Accepted author manuscript, 484 KB

Multi-modal Retinal Biomarkers for Vascular Dementia; Developing and Enabling Image Analysis Tools (Joint with University of Edinburgh)
Doney, A. (Investigator), McKenna, S. (Investigator) & Trucco, M. (Investigator)
Engineering and Physical Sciences Research Council
30/04/15 → 29/08/18
Project: Research

Cite this

Fetit, A., Manivannan, S., McGrory, S., Ballerini, L., Doney, A., MacGillivray, T. J., Deary, I. J., Wardlaw, J. M., Doubal, F., McKay, G. J., McKenna, S., & Trucco, E. (2017). Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population. In J. Cardoso, T. Arbel, A. Melbourne, H. Bogunovic, P. Moeskops, X. Chen, E. Schwartz, M. Garvin, E. Robinson, E. Trucco, M. Ebner, Y. Xu, A. Makropoulos, A. Desjardin, & T. Vercauteren (Eds.), Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI2017, Quebec City, QC, Canada, September 14, Proceedings (pp. 150-158). (Lecture Notes in Computer Science; Vol. 10554). Springer . https://doi.org/10.1007/978-3-319-67561-9_17, https://doi.org/10.1007/978-3-319-67561-9

Fetit, Ahmed ; Manivannan, Siyamalan ; McGrory, Sarah et al. / Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population. Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI2017, Quebec City, QC, Canada, September 14, Proceedings. editor / J. Cardoso ; T. Arbel ; A. Melbourne ; H. Bogunovic ; P. Moeskops ; X. Chen ; E. Schwartz ; M. Garvin ; E. Robinson ; E. Trucco ; M. Ebner ; Y. Xu ; A. Makropoulos ; A. Desjardin ; T. Vercauteren. Switzerland : Springer , 2017. pp. 150-158 (Lecture Notes in Computer Science).

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abstract = "Dementia is a devastating disease, and has severe implications on affected individuals, their family and wider society. A growing body of literature is studying the association of retinal microvasculature measurement with dementia. We present a pilot study testing the strength of groups of conventional (semantic) and texture-based (non-semantic) measurements extracted from retinal fundus camera images to classify patients with and without dementia. We performed a 500-trial bootstrap analysis with regularized logistic regression on a cohort of 1,742 elderly diabetic individuals (median age 72.2). Age was the strongest predictor for this elderly cohort. Semantic retinal measurements featured in up to 81% of the bootstrap trials, with arterial caliber and optic disk size chosen most often, suggesting that they do complement age when selected together in a classifier. Textural features were able to train classifiers that match the performance of age, suggesting they are potentially a rich source of information for dementia outcome classification",

keywords = "Retina, Dementia, Microvasculature, Classification, Biomarkers",

author = "Ahmed Fetit and Siyamalan Manivannan and Sarah McGrory and Lucia Ballerini and Alexander Doney and MacGillivray, {Thomas J.} and Deary, {Ian J.} and Wardlaw, {Joanna M.} and Fergus Doubal and McKay, {Gareth J.} and Stephen McKenna and Emanuele Trucco",

note = "This work was supported by EPSRC grant EP/M005976/1 “Multi-modal retinal biomarkers for vascular dementia”. Added DOI to chapter.; 4th MICCAI Workshop on Ophthalmic Medical Image Analysis ; Conference date: 10-09-2017 Through 14-09-2017",

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Fetit, A, Manivannan, S, McGrory, S, Ballerini, L, Doney, A, MacGillivray, TJ, Deary, IJ, Wardlaw, JM, Doubal, F, McKay, GJ, McKenna, S & Trucco, E 2017, Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population. in J Cardoso, T Arbel, A Melbourne, H Bogunovic, P Moeskops, X Chen, E Schwartz, M Garvin, E Robinson, E Trucco, M Ebner, Y Xu, A Makropoulos, A Desjardin & T Vercauteren (eds), Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI2017, Quebec City, QC, Canada, September 14, Proceedings. Lecture Notes in Computer Science, vol. 10554, Springer , Switzerland, pp. 150-158, 4th MICCAI Workshop on Ophthalmic Medical Image Analysis , Quebec, Canada, 10/09/17. https://doi.org/10.1007/978-3-319-67561-9_17, https://doi.org/10.1007/978-3-319-67561-9

Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population. / Fetit, Ahmed; Manivannan, Siyamalan; McGrory, Sarah et al.
Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI2017, Quebec City, QC, Canada, September 14, Proceedings. ed. / J. Cardoso; T. Arbel; A. Melbourne; H. Bogunovic; P. Moeskops; X. Chen; E. Schwartz; M. Garvin; E. Robinson; E. Trucco; M. Ebner; Y. Xu; A. Makropoulos; A. Desjardin; T. Vercauteren. Switzerland: Springer , 2017. p. 150-158 (Lecture Notes in Computer Science; Vol. 10554).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

AU - Fetit, Ahmed

AU - Manivannan, Siyamalan

AU - McGrory, Sarah

AU - Ballerini, Lucia

AU - Doney, Alexander

AU - MacGillivray, Thomas J.

AU - Deary, Ian J.

AU - Wardlaw, Joanna M.

AU - Doubal, Fergus

AU - McKay, Gareth J.

AU - McKenna, Stephen

AU - Trucco, Emanuele

N1 - This work was supported by EPSRC grant EP/M005976/1 “Multi-modal retinal biomarkers for vascular dementia”. Added DOI to chapter.

PY - 2017/9/14

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N2 - Dementia is a devastating disease, and has severe implications on affected individuals, their family and wider society. A growing body of literature is studying the association of retinal microvasculature measurement with dementia. We present a pilot study testing the strength of groups of conventional (semantic) and texture-based (non-semantic) measurements extracted from retinal fundus camera images to classify patients with and without dementia. We performed a 500-trial bootstrap analysis with regularized logistic regression on a cohort of 1,742 elderly diabetic individuals (median age 72.2). Age was the strongest predictor for this elderly cohort. Semantic retinal measurements featured in up to 81% of the bootstrap trials, with arterial caliber and optic disk size chosen most often, suggesting that they do complement age when selected together in a classifier. Textural features were able to train classifiers that match the performance of age, suggesting they are potentially a rich source of information for dementia outcome classification

AB - Dementia is a devastating disease, and has severe implications on affected individuals, their family and wider society. A growing body of literature is studying the association of retinal microvasculature measurement with dementia. We present a pilot study testing the strength of groups of conventional (semantic) and texture-based (non-semantic) measurements extracted from retinal fundus camera images to classify patients with and without dementia. We performed a 500-trial bootstrap analysis with regularized logistic regression on a cohort of 1,742 elderly diabetic individuals (median age 72.2). Age was the strongest predictor for this elderly cohort. Semantic retinal measurements featured in up to 81% of the bootstrap trials, with arterial caliber and optic disk size chosen most often, suggesting that they do complement age when selected together in a classifier. Textural features were able to train classifiers that match the performance of age, suggesting they are potentially a rich source of information for dementia outcome classification

KW - Retina

KW - Dementia

KW - Microvasculature

KW - Classification

KW - Biomarkers

U2 - 10.1007/978-3-319-67561-9_17

DO - 10.1007/978-3-319-67561-9_17

M3 - Conference contribution

SN - 9783319675602

T3 - Lecture Notes in Computer Science

SP - 150

EP - 158

BT - Fetal, Infant and Ophthalmic Medical Image Analysis

A2 - Cardoso, J.

A2 - Arbel, T.

A2 - Melbourne, A.

A2 - Bogunovic, H.

A2 - Moeskops, P.

A2 - Chen, X.

A2 - Schwartz, E.

A2 - Garvin, M.

A2 - Robinson, E.

A2 - Trucco, E.

A2 - Ebner, M.

A2 - Xu, Y.

A2 - Makropoulos, A.

A2 - Desjardin, A.

A2 - Vercauteren, T.

PB - Springer

CY - Switzerland

T2 - 4th MICCAI Workshop on Ophthalmic Medical Image Analysis

Y2 - 10 September 2017 through 14 September 2017

ER -

Fetit A, Manivannan S, McGrory S, Ballerini L, Doney A, MacGillivray TJ et al. Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population. In Cardoso J, Arbel T, Melbourne A, Bogunovic H, Moeskops P, Chen X, Schwartz E, Garvin M, Robinson E, Trucco E, Ebner M, Xu Y, Makropoulos A, Desjardin A, Vercauteren T, editors, Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI2017, Quebec City, QC, Canada, September 14, Proceedings. Switzerland: Springer . 2017. p. 150-158. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-67561-9_17, 10.1007/978-3-319-67561-9

Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

Abstract

Publication series

Conference

Keywords

UN SDGs

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Projects

Multi-modal Retinal Biomarkers for Vascular Dementia; Developing and Enabling Image Analysis Tools (Joint with University of Edinburgh)

Cite this