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Evaluation of a retinal deep phenotyping platform to detect the likely cerebral amyloid PET status in humans
Author(s) -
Soucy JeanPaul,
Chevrefils Claudia,
Osseiran Sam,
Sylvestre JeanPhilippe,
Beaulieu Sylvain,
Pascoal Tharick A,
Arbour Jean Daniel,
Rhéaume MarcAndré,
Villeneuve Sylvia,
Nasreddine Ziad S,
RosaNeto Pedro,
Gauthier Serge,
Robillard Alain,
Chayer Céline,
Black Sandra E.,
Kertes Peter J,
Shahawy Hossam El,
Chen John J,
Knopman David S.,
Lowe Val J,
Lesage Frédéric
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.043395
Subject(s) - artificial intelligence , hyperspectral imaging , retinal , gold standard (test) , biomarker , feature selection , neuroimaging , classifier (uml) , pattern recognition (psychology) , medicine , positron emission tomography , computer science , pathology , nuclear medicine , radiology , ophthalmology , biology , psychiatry , biochemistry
Background Translating recent advances in the diagnosis of Alzheimer’s disease (AD) and other dementias to clinical practice using biomarker‐based approaches requires accessible, affordable biomarkers. This protocol tested the performance of a Retinal Deep Phenotyping platform for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in human subjects. This platform generates data rich retinal reflectance images captured non‐invasively with a Metabolic Hyperspectral Retinal Camera (MHRC) yielding specific spatial‐spectral features which are analyzed by a machine learning algorithm relatively to a recognized gold standard biomarker (in this case PET amyloid imaging). Methods 134 subjects (50 years and older) from 3 clinical sites were included, 94 with normal, 40 with abnormal cognition, imaged with the MHRC and amyloid PET (unanimous visual reads from a panel of 3 expert reviewers; 42 amyloid positive, 92 negative). Over 600 spatial‐spectral features were extracted from hyperspectral retinal images obtained at 450‐900 nm. After a feature selection based on F‐score ranking, a classifier was trained on the datasets from 87 patients (2/3 of the cohort) using the most discriminating features. The classification performance was then evaluated with the datasets from the remaining 47 patients. Results Resubstituting the training set of 87 patients into the classifier yielded sensitivity and specificity values of 88% and 91%, respectively. The training misclassification rate was predicted using the k ‐fold loss metric, with a predicted error rate of 19%. To verify this performance, the test set of 47 patients was classified by the model resulting in sensitivity and specificity values of 80% and 81%, respectively. Conclusions The Retinal Deep Phenotyping platform shows promise for detecting the likely cerebral amyloid PET status in human subjects and could serve as a screening tool to identify subjects in the AD continuum, for instance in a clinical or drug development context. The platform could also be used for the detection of other biomarkers involved in cognitive decline.