Diagnoses or Predicts Alzheimer’s Disease Using Non-Invasive, Low-Cost Retinal Images
This machine learning system detects or predicts the onset of Alzheimer’s disease by assessing the presence of pathologies associated with the disease in retinal fundus images. Alzheimer’s disease is the most common neurodegenerative disease and is expected to affect 13-16 million people by the year 2050. Alzheimer’s disease causes neural damage that is generally irreversible. Clinical treatment of Alzheimer’s disease focuses on slowing neural degeneration, and early detection is critical for achieving effective treatment. Current detection methods are often not implemented until after the onset of symptoms because they are either invasive, costly, or inaccurate before significant disease progression.
Researchers at the University of Florida have developed a machine-learning algorithm that detects Alzheimer’s disease by analyzing retinal fundus images. After making a disease determination, the algorithm produces a saliency map of the retinal fundus that highlights areas of the image that were critical to making the determination. Unlike current Alzheimer screening methods, retinal images are non-invasive, relatively inexpensive to obtain, and can show Alzheimer’s onset prior to symptoms.
Application
Machine learning algorithm that detects or predicts the onset of Alzheimer’s disease using low-cost, non-invasive retinal images
Advantages
- Uses retinal images, providing a non-invasive, low-cost screening method for detecting Alzheimer’s in vivo
- Can detect and monitor Alzheimer’s earlier than available screening methods, increasing the possibility for achieving effective treatment or intervention
- Machine learning methods are capable of finding deeper level features missed by the human eye, producing a clear classification result for the patient
- Produces a saliency map of the retinal fundus that can be viewed and evaluated by a human medical professional
Technology
Retinal fundus images have been studied for their potential to detect Alzheimer’s disease previously, but by-hand measurement and labeling of retinal features is labor intensive and prone to human error. Recent advances in machine learning have used retinal fundus imagery to detect a number of diseases including glaucoma and anemia. This machine-learning algorithm is able to detect or predict the onset of Alzheimer’s disease by examining the retinal vasculature for pathologies associated with the disease.
Brochure
