Sequentially Optimized Reconstruction Strategy: A Meta-Strategy for Perimetry Testing

Kucur, Serife Seda; Sznitman, Raphael (2017). Sequentially Optimized Reconstruction Strategy: A Meta-Strategy for Perimetry Testing. PLoS ONE, 12(10), pp. 1-20. Public Library of Science 10.1371/journal.pone.0185049

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Perimetry testing is an automated method to measure visual function and is heavily used for diagnosing ophthalmic and neurological conditions. Its working principle is to sequentially query a subject about perceived light using different brightness levels at different visual field locations. At a given location, this query-patient-feedback process is expected to converge at a perceived sensitivity, such that a shown stimulus intensity is observed and reported 50% of the time. Given this inherently time-intensive and noisy process, fast testing strategies are necessary in order to measure existing regions more effectively and reliably. In this work, we present a novel meta-strategy which relies on the correlative nature of visual field locations in order to strongly reduce the necessary number of locations that need to be examined. To do this, we sequentially determine locations that most effectively reduce visual field estimation errors in an initial training phase. We then exploit these locations at examination time and show that our approach can easily be combined with existing perceived sensitivity estimation schemes to speed up the examinations. Compared to state-of-the-art strategies, our approach shows marked performance gains with a better accuracy-speed trade-off regime for both mixed and sub-populations.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Image Guided Therapy > ARTORG Center - Ophthalmic Technology Lab

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kucur, Serife Seda and Sznitman, Raphael

Subjects:

500 Science > 570 Life sciences; biology
600 Technology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1932-6203

Publisher:

Public Library of Science

Language:

English

Submitter:

Raphael Sznitman

Date Deposited:

21 Nov 2017 09:29

Last Modified:

21 Nov 2017 09:29

Publisher DOI:

10.1371/journal.pone.0185049

PubMed ID:

29028838

BORIS DOI:

10.7892/boris.105716

URI:

https://boris.unibe.ch/id/eprint/105716

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