Retinal Biomarker Discovery for Dementia in an Elderly Diabetic Population

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

Y1 - 2017/9/14

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 -