In recent study, researchers focused on a novel noninvasive tool that examines the eye to identify early signs of the Alzheimer’s disease.
In the study, published in Acta Neuropathologica Communications, researchers outlined the use of hyperspectral retinal imaging (HSI), a cutting-edge technique designed to capture detailed light patterns from the retina, which offers a possible method for diagnosing Alzheimer's disease (AD) before symptoms become evident. Additionally, the researchers noted that HSI could also be used for diagnosing age-related macular degeneration (AMD), another neurodegenerative condition.
The researchers noted that evidence has revealed that AD’s effects could extend to the retina, and detailed that hallmarks of Alzheimer’s, such as amyloid-beta plaques and tau protein tangles, which are typically found in the brain, have also been identified in the retinas of patients with AD. Current diagnostic tests for AD, including positron emission tomography scans and cerebrospinal fluid analysis, are invasive and can be cost ineffective, making them poorly suited for widespread screening. However, using HSI as a noninvasive approach could allow for more routine monitoring.
HSI can capture both the spatial and spectral information of light reflected from the retina. By analyzing how light interacts with tissue, it can detect subtle changes in the retina that signal disease processes. Unlike traditional retinal imaging, which uses just a few colors of light, HSI is capable of collecting data across hundreds of wavelengths, creating a three-dimensional data set that allows for the identification of retinal biomarkers.
Despite its potential, the researchers noted several challenges to overcome before HSI can be widely implemented in clinical settings. One of the issues involves the variability in spectral data collected from different patients, which can complicate diagnoses. To address this, the researchers are currently exploring depth-resolved HSI, which can better isolate signals from the specific layers of the retina where Alzheimer’s disease biomarkers are most likely to accumulate.
As with many medical imaging techniques, the sheer volume of data generated by HSI may present a computational challenge. To help make sense of this data, the researchers are turning to artificial intelligence (AI). Deep learning algorithms, particularly convolutional neural networks, have shown promise in analyzing hyperspectral retinal images, identifying patterns that may be missed by the human eye. These AI tools could make HSI more practical for widespread clinical use, allowing for faster and more accurate diagnoses.
Author disclosures can be found in the published research.