Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer’s disease patients

Autor: Lieve Moons, Karel Van Keer, Jan Theunis, Lies De Groef, Gordana Sunaric-Mégevand, Ingeborg Stalmans, Sophie Lemmens, Wouter Charle, Arnout Standaert, Jan Van Eijgen, Rik Vandenberghe, Mathieu Vandenbulcke, Rose Bruffaerts, Danilo Andrade De Jesus, Toon Van Craenendonck, Murali Jayapala, Patrick De Boever
Přispěvatelé: Theunis, Jan/0000-0002-4191-5667, De Boever, Patrick/0000-0002-5197-8215, Lemmens, Sophie, Van Craenendonck, Toon, Van Eijgen, Jan, De Groef, Lies, BRUFFAERTS, Rose, de Jesus, Danilo Andrade, Charle, Wouter, Jayapala, Murali, Sunaric-Megevand, Gordana, Standaert, Arnout, THEUNIS, Jan, Van Keer, Karel, Vandenbulcke, Mathieu, Moons, Lieve, Vandenberghe, Rik, Stalmans, Ingeborg, DE BOEVER, Patrick
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Amyloid-beta (Aβ)
Hyperspectral imaging
Cognitive Neuroscience
Nerve fiber layer
Overfitting
lcsh:RC346-429
Retina
)
lcsh:RC321-571
s disease
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Optical coherence tomography
Alzheimer Disease
Machine learning
Medicine
Humans
Neurodegeneration
Alzheimer’
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
lcsh:Neurology. Diseases of the nervous system
Receiver operating characteristic
medicine.diagnostic_test
business.industry
Research
Brain
Amyloid-beta (Aβ
Retinal
Pattern recognition
Biomarker
Linear discriminant analysis
Confidence interval
medicine.anatomical_structure
Cognitive impairment
Neurology
chemistry
ROC Curve
030221 ophthalmology & optometry
Neurology (clinical)
Artificial intelligence
Human medicine
business
Alzheimer’s disease
030217 neurology & neurosurgery
Biomarkers
Tomography
Optical Coherence
Zdroj: Alzheimer's research & therapy
Alzheimer's Research & Therapy
Alzheimer’s Research & Therapy, Vol 12, Iss 1, Pp 1-13 (2020)
ISSN: 1758-9193
Popis: Introduction The eye offers potential for the diagnosis of Alzheimer's disease (AD) with retinal imaging techniques being explored to quantify amyloid accumulation and aspects of neurodegeneration. To assess these changes, this proof-of-concept study combined hyperspectral imaging and optical coherence tomography to build a classification model to differentiate between AD patients and controls. Methods In a memory clinic setting, patients with a diagnosis of clinically probable AD (n = 10) or biomarker-proven AD (n = 7) and controls (n = 22) underwent non-invasive retinal imaging with an easy-to-use hyperspectral snapshot camera that collects information from 16 spectral bands (460-620 nm, 10-nm bandwidth) in one capture. The individuals were also imaged using optical coherence tomography for assessing retinal nerve fiber layer thickness (RNFL). Dedicated image preprocessing analysis was followed by machine learning to discriminate between both groups. Results Hyperspectral data and retinal nerve fiber layer thickness data were used in a linear discriminant classification model to discriminate between AD patients and controls. Nested leave-one-out cross-validation resulted in a fair accuracy, providing an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [0.60-0.89]). Inner loop results showed that the inclusion of the RNFL features resulted in an improvement of the area under the receiver operating characteristic curve: for the most informative region assessed, the average area under the receiver operating characteristic curve was 0.70 (95% confidence interval [0.55, 0.86]) and 0.79 (95% confidence interval [0.65, 0.93]), respectively. The robust statistics used in this study reduces the risk of overfitting and partly compensates for the limited sample size. Conclusions This study in a memory-clinic-based cohort supports the potential of hyperspectral imaging and suggests an added value of combining retinal imaging modalities. Standardization and longitudinal data on fully amyloid-phenotyped cohorts are required to elucidate the relationship between retinal structure and cognitive function and to evaluate the robustness of the classification model. Sophie Lemmens and Jan Van Eijgen are holders of a joint VITO-UZ Leuven PhD grant. Part of this research work has been funded in the context of the VITO-UZ Leuven HERALD project that was granted by the ATTRACT consortium, which received funding from the European Union's Horizon 2020 Research and Innovation Programme (2014-2020). Lies De Groef and Rose Bruffaerts are postdoctoral fellows of the Research Foundation Flanders. Lemmens, S (corresponding author), Univ Hosp UZ Leuven, Dept Ophthalmol, Herestr 49, B-3000 Leuven, Belgium ; Katholieke Univ Leuven, Res Grp Ophthalmol, Biomed Sci Grp, Dept Neurosci, Herestr 49, B-3000 Leuven, Belgium. VITO Flemish Inst Technol Res, Hlth Unit, Boeretang 200, B-2400 Mol, Belgium. sophie.1.lemmens@uzleuven.be
Databáze: OpenAIRE
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