Prediction of visual function from automatically quantified optical coherence tomography biomarkers in patients with geographic atrophy using machine learning.

Biomarkers Cross-Sectional Studies Geographic Atrophy / diagnostic imaging Humans Machine Learning Tomography, Optical Coherence / methods

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
16 Sep 2022
Historique:
received: 22 06 2022
accepted: 29 08 2022
entrez: 16 9 2022
pubmed: 17 9 2022
medline: 21 9 2022
Statut: epublish

Résumé

Geographic atrophy (GA) is a vision-threatening manifestation of age-related macular degeneration (AMD), one of the leading causes of blindness globally. Objective, rapid, reliable, and scalable quantification of GA from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and clinical endpoints for therapy development. Such automatically quantified biomarkers on OCT are likely to further elucidate structure-function correlation in GA and thus the pathophysiological mechanisms of disease development and progression. In this work, we aimed to predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in GA. A post-hoc analysis of data from a clinical trial and routine clinical care was conducted. A deep-learning automated segmentation model was applied on OCT scans from 476 eyes (325 patients) with GA. A separate machine learning prediction model (Random Forest) used the resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under standard (VA) and low luminance (LLVA). The primary outcome was regression coefficient (r

Identifiants

DOI: 10.1038/s41598-022-19413-z PMID: 36114218 PMC: PMC9481631
pubmed: 36114218
doi: 10.1038/s41598-022-19413-z
pii: 10.1038/s41598-022-19413-z
pmc: PMC9481631
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

15565

Subventions

Organisme : Medical Research Council
ID : MR/T019050/1
Pays : United Kingdom

Informations de copyright

© 2022. The Author(s).

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Auteurs

Konstantinos Balaskas (K)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK. k.balaskas@nhs.net.

S Glinton (S)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

T D L Keenan (TDL)

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.

L Faes (L)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

B Liefers (B)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.
Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands.

G Zhang (G)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

N Pontikos (N)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

R Struyven (R)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

S K Wagner (SK)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

A McKeown (A)

Apellis Pharmaceuticals, Inc, Waltham, MA, USA.

P J Patel (PJ)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

P A Keane (PA)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

D J Fu (DJ)

NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.

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Classifications MeSH

questionsmedicales.fr › Facteurs biologiques › Marqueurs biologiques › Prediction of visual function from...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études transversales › Prediction of visual function from...
questionsmedicales.fr › Maladies de l'oeil › Rétinopathies › Dégénérescence de la rétine › Dégénérescence maculaire › Atrophie géographique › Prediction of visual function from...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Prediction of visual function from...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Prediction of visual function from...
questionsmedicales.fr › Techniques d'investigation › Imagerie optique › Tomographie optique › Tomographie par cohérence optique › Prediction of visual function from...