Stage-independent biomarkers for Alzheimer’s disease from the living retina: an animal study

The early diagnosis of neurodegenerative disorders is still an open issue despite the many efforts to address this problem. In particular, Alzheimer’s disease (AD) remains undiagnosed for over a decade before the first symptoms. Optical coherence tomography (OCT) is now common and widely available a...

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Bibliographic Details
Published in:Scientific reports 2022-08, Vol.12 (1), p.13667-7, Article 13667
Main Authors: Ferreira, Hugo, Serranho, Pedro, Guimarães, Pedro, Trindade, Rita, Martins, João, Moreira, Paula I., Ambrósio, António Francisco, Castelo-Branco, Miguel, Bernardes, Rui
Format: Article
Language:English
Subjects:
631/378/1689/1283
631/378/1689/364
639/166/985
692/53/2421
692/53/2422
692/53/2423
Alzheimer Disease - diagnostic imaging
Alzheimer's disease
Animals
Animals, Laboratory
Biomarkers
Diagnosis
Humanities and Social Sciences
Mice
Mice, Transgenic
multidisciplinary
Neural networks
Neurodegeneration
Neurodegenerative diseases
Retina
Retina - diagnostic imaging
Rodents
Science
Science (multidisciplinary)
Tomography, Optical Coherence - methods
Transgenic mice
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Summary:The early diagnosis of neurodegenerative disorders is still an open issue despite the many efforts to address this problem. In particular, Alzheimer’s disease (AD) remains undiagnosed for over a decade before the first symptoms. Optical coherence tomography (OCT) is now common and widely available and has been used to image the retina of AD patients and healthy controls to search for biomarkers of neurodegeneration. However, early diagnosis tools would need to rely on images of patients in early AD stages, which are not available due to late diagnosis. To shed light on how to overcome this obstacle, we resort to 57 wild-type mice and 57 triple-transgenic mouse model of AD to train a network with mice aged 3, 4, and 8 months and classify mice at the ages of 1, 2, and 12 months. To this end, we computed fundus images from OCT data and trained a convolution neural network (CNN) to classify those into the wild-type or transgenic group. CNN performance accuracy ranged from 80 to 88% for mice out of the training group’s age, raising the possibility of diagnosing AD before the first symptoms through the non-invasive imaging of the retina.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-18113-y