RetiSpec, a Toronto-based startup, has developed an artificial intelligence (AI) algorithm that can analyze eye scanner results and detect signs of Alzheimer’s up to 20 years before symptoms appear. This breakthrough comes as part of a larger effort by startups and researchers to utilize AI in unraveling the mysteries of this debilitating neurological disease that affects millions of Americans.
While individual hallmarks of Alzheimer’s have been studied for years, the exact causes of the disease remain unknown. AI could potentially usher in a new era in the diagnosis of Alzheimer’s, a disease that has been historically difficult to identify and treat.
Neurovision, a company based in Sacramento, California, is also using machine learning to develop retinal scans and blood tests for early detection of Alzheimer’s and other forms of dementia. By analyzing eye scans for anomalies associated with Alzheimer’s, such as protein buildup or twisted blood vessels, Neurovision’s AI model aims to identify individuals at risk.
Traditional diagnostic methods often struggle to discern these signs in scans. Plaque deposits can be very small, and the human eye may not be able to distinguish them well. However, AI algorithms can overcome these limitations, providing more accurate results.
At the University of Arizona College of Medicine, neurology associate professor Rui Chang has developed an AI model that aims to identify genetic triggers linked to Alzheimer’s. This approach enables AI to process vast amounts of information and identify patterns that may go unnoticed by humans. Chang’s model has significantly reduced research time and will soon undergo clinical trials through his company, Path-Biotech.
AI technologies have the potential to streamline the diagnostic process, making it faster and more cost-effective. For example, RetiSpec’s AI can read scans from a camera attached to existing machines in optometrists’ offices, providing instantaneous results. In a recent study of 271 patients, the AI was 80% accurate in detecting signature optical markers associated with amyloid in the brain.
While AI tools have shown promise in clinical testing, they may encounter difficulties in real-life scenarios. The learning process of biotech AI models relies heavily on vast amounts of data. However, researchers and developers are continually refining and improving these models to enhance their performance.
In conclusion, AI is spearheading significant advancements in the early detection of Alzheimer’s. By leveraging the power of AI algorithms, researchers and startups are making strides in understanding and potentially treating this devastating disease.
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