Retinal biomarker discovery for dementia in an elderly diabetic population

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

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

Retinal biomarker discovery for dementia in an elderly diabetic population

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

^*Corresponding author for this work

Centre for Public Health

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 - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings
Publisher	Springer Verlag
Pages	150-158
Number of pages	9
Volume	10554 LNCS
ISBN (Print)	9783319675602
DOIs	https://doi.org/10.1007/978-3-319-67561-9_17
Publication status	Published - 10 Sept 2017
Event	International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 14 Sept 2017 → 14 Sept 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10554
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	14/09/2017 → 14/09/2017

Keywords

Biomarkers
Classification
Dementia
Microvasculature
Retina

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

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

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Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population
© 2017 Springer International Publishing. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 859 KBLicence: Unspecified

Cite this

Fetit, A. E., Manivannan, S., McGrory, S., Ballerini, L., Doney, A., MacGillivray, T. J., Deary, I. J., Wardlaw, J. M., Doubal, F., McKay, G. J., McKenna, S. J., & Trucco, E. (2017). Retinal biomarker discovery for dementia in an elderly diabetic population. In Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10554 LNCS, pp. 150-158). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10554). Springer Verlag. https://doi.org/10.1007/978-3-319-67561-9_17

Fetit, Ahmed E. ; 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 MICCAI 2017, Proceedings. Vol. 10554 LNCS Springer Verlag, 2017. pp. 150-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{97e9dbaf013440e6b627fe3690d2d92d,

title = "Retinal biomarker discovery for dementia in an elderly diabetic population",

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

author = "Fetit, \{Ahmed E.\} 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 McKenna, \{Stephen J.\} and Emanuele Trucco",

year = "2017",

month = sep,

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doi = "10.1007/978-3-319-67561-9\_17",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings",

address = "Germany",

note = "International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 14-09-2017 Through 14-09-2017",

}

Fetit, AE, Manivannan, S, McGrory, S, Ballerini, L, Doney, A, MacGillivray, TJ, Deary, IJ, Wardlaw, JM, Doubal, F, McKay, GJ, McKenna, SJ & Trucco, E 2017, Retinal biomarker discovery for dementia in an elderly diabetic population. in Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings. vol. 10554 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10554, Springer Verlag, pp. 150-158, International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 14/09/2017. https://doi.org/10.1007/978-3-319-67561-9_17

Retinal biomarker discovery for dementia in an elderly diabetic population. / Fetit, Ahmed E.; 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 MICCAI 2017, Proceedings. Vol. 10554 LNCS Springer Verlag, 2017. p. 150-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 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 E.

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 J.

AU - Trucco, Emanuele

PY - 2017/9/10

Y1 - 2017/9/10

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 - Biomarkers

KW - Classification

KW - Dementia

KW - Microvasculature

KW - Retina

UR - https://www.scopus.com/pages/publications/85029805311

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

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

M3 - Conference contribution

AN - SCOPUS:85029805311

SN - 9783319675602

VL - 10554 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 150

EP - 158

BT - Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings

PB - Springer Verlag

T2 - International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017

Y2 - 14 September 2017 through 14 September 2017

ER -

Fetit AE, Manivannan S, McGrory S, Ballerini L, Doney A, MacGillivray TJ et al. Retinal biomarker discovery for dementia in an elderly diabetic population. In Fetal, Infant and Ophthalmic Medical Image Analysis - International Workshop, FIFI 2017 and 4th International Workshop, OMIA 2017 Held in Conjunction with MICCAI 2017, Proceedings. Vol. 10554 LNCS. Springer Verlag. 2017. p. 150-158. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67561-9_17

Retinal biomarker discovery for dementia in an elderly diabetic population

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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