A retinal deep phenotypingTM platform to predict the cerebral amyloid PET status in older adults. (31st December 2021)

• Record Type: Journal Article
• Authors: Soucy, Jean‐Paul; Chevrefils, Claudia; Osseiran, Sam; Sylvestre, Jean‐Philippe; Lesage, Frédéric; Beaulieu, Sylvain; Pascoal, Tharick A.; Provost, Karine; Arbour, Jean Daniel; Rhéaume, Marc‐André; Villeneuve, Sylvia; Nasreddine, Ziad S.; Rosa‐Neto, Pedro; Gauthier, Serge; Robillard, Alain; Chayer, Céline; Black, Sandra E.; Kertes, Peter J.; El Shahawy, Hossam; Scott, Christopher J.M.; Bhan, Aparna; Martins, Ralph N.; Shah, Tejal M.; Gupta, Sunil M.; Calvin, Kirsten; Hsu, Gregory; Lowe, Val J.; Chen, John J.; Ritter, Aaron
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.054582
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: As the only optically accessible part of the central nervous system, the retina represents an intriguing opportunity for the detection of biomarkers for Alzheimer's disease (AD). This study evaluated the performance of the Retinal Deep Phenotyping TM platform, a digital biomarker platform comprising a hyperspectral retinal camera and image analysis algorithms, for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in older adults. A set of phenotypic features that correlates with the cerebral amyloid status as determined by amyloid PET scan were identified and used to train a classifying algorithm. Method: Hyperspectral retinal images acquired with a Mydriatic Hyperspectral Retinal Camera from 194 participants (age ≥ 50 years), including cognitively normal and cognitively impaired (mild cognitive impairment and dementia) across 5 imaging sites were processed in order to train the model. Of these 194 participants, 73 individuals (38%) were amyloid‐positive, as confirmed by unanimous readings of PET scans by a panel of 3 expert reviewers. The pre‐processed hyperspectral images were segmented into various anatomical sites, and a texture‐based approach was used to extract several thousands of spatial‐spectral features. The most relevant features for the classification task were selected using a minimum redundancy maximum relevance (MRMR) algorithm and used to train a linear support vector machine (SVM) classifier. A nested, cross‐validation technique was used to evaluate the performance of the classifier. Result: The resulting model based on the 17 most significant features showed high performance to discriminate between amyloid positive and negative subjects with an area under the receiver operating curve (AUCROC ) of 0.87 (95% CI: 0.83 – 0.92). Conclusion: The Retinal Deep Phenotyping TM platform shows promise for detecting the likely cerebral amyloid PET status in adults 50 years and older from a simple, non‐invasive retinal scan and could provide an accessible means to identify individuals with abnormal cerebral amyloid in a clinical or drug development context. This phenotyping platform provides a flexible approach that could also be used for the detection of multiple biomarkers involved in cognitive decline from the same hyperspectral images of the retina.