The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."

The growing involvement of technologies such as AI and computer vision in healthcare enables health experts to predict and track the advancement of Alzheimer's disease in patients. The mere possibility of being diagnosed with Alzheimer's is enough to fill patients' minds with a deep sense of foreboding. After all, this is a disease that increasingly limits the functioning of a patient's brain, leading them, eventually, into a perpetually vegetative state of existence. In 2021, one in nine persons in the US aged 65 and older are living with Alzheimer's dementia. The progression of Alzheimer's in a patient is closely linked with their age, and hence, at least for now, there is no known cure for it.

Artificial intelligence can predict Alzheimer's at least two years in advance, according to new research. The technique works by spotting hidden patterns in the data and learning who is most at risk. A study involving more than 15,300 people found it was 92 percent accurate. The neural network would change the way dementia is diagnosed - helping doctors detect it sooner. Treatments would start much earlier.

IBM researchers then used NLP to analyse the participants' language sample transcripts. The model picked up tiny subtleties and changes in discourses that are generally missed if done manually. Based on this, IBM researchers trained the ML model to account for multiple variables affecting the results. Lastly, they drew on data from the subjects at the Framingham Heart Study, where the participants are assessed through two-minute Mini-Mental State Examination speech tests every four years and neuropsychological exams every year. CTT examples from FHS, including an unimpaired sample (a), an impaired sample showing telegraphic speech and lack of punctuation (b), and an even more impaired sample showing in addition significant misspellings and minimal grammatical complexity, e.g.

The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."

A form of artificial intelligence designed to interpret a combination of retinal images was able to successfully identify a group of patients who were known to have Alzheimer's disease, suggesting the approach could one day be used as a predictive tool, according to an interdisciplinary study from Duke University. The novel computer software looks at retinal structure and blood vessels on images of the inside of the eye that have been correlated with cognitive changes. The findings, appearing last week in the British Journal of Ophthalmology, provide proof-of-concept that machine learning analysis of certain types of retinal images has the potential to offer a non-invasive way to detect Alzheimer's disease in symptomatic individuals. "Diagnosing Alzheimer's disease often relies on symptoms and cognitive testing," said senior author Sharon Fekrat, M.D., retina specialist at the Duke Eye Center. "Additional tests to confirm the diagnosis are invasive, expensive, and carry some risk. Having a more accessible method to identify Alzheimer's could help patients in many ways, including improving diagnostic precision, allowing entry into clinical trials earlier in the disease course, and planning for necessary lifestyle adjustments."

A new AI model, developed by IBM Research and Pfizer, has used short, non-invasive and standardized speech tests to help predict the eventual onset of Alzheimer's disease within healthy people with an accuracy of 0.7 and an AUC of 0.74 (area under the curve). These predictions were made against data samples from a group of healthy individuals who eventually did or did not develop the disease later in life, allowing researchers to verify the accuracy of the AI model's prediction. This is a significant increase over predictions based on clinical scales (59%), which is a prediction based on other available biomedical data from a patient, as well as random choice (50%). The model uses natural language processing to analyze one- to two-minute speech samples from a brief, clinically administered cognitive test. These short samples of language data were provided by the Framingham Heart Study, a long-running study tracking various aspects of health in more than 5,000 people and their families since 1948.

Alzheimer's is a crippling degenerative disease, but the answer to early diagnosis might lie in speech. I was afraid my grandmother wouldn't remember who I was the last time I saw her in person. She looked small and frail in the wheelchair but I could still see the sparkle in her eyes. Our relationship was complicated, but when she said she remembered me, none of it mattered any more. I sat by her wheelchair and tried to cram in a decade of memories and happenings.

Pfizer and IBM researchers claim to have developed a machine learning technique that can predict Alzheimer's disease years before symptoms develop. By analyzing small samples of language data obtained from clinical verbal tests, the team says their approach achieved 71% accuracy when tested against a group of cognitively healthy people. Alzheimer's disease begins with vague, often misinterpreted signs of mild memory loss followed by a slow, progressively serious decline in cognitive ability and quality of life. According to the nonprofit Alzheimer's Association, more than 5 million Americans of all ages have Alzheimer's, and every state is expected to see at least a 14% rise in the prevalence of Alzheimer's between 2017 and 2025. Due to the nature of Alzheimer's disease and how it takes hold in the brain, it's likely that the best way to delay its onset is through early intervention.

We haven't reached widespread adoption of AI in enterprises yet, but it's coming. More enterprises have adopted stream processing, though, because it can enable a variety of mission-critical use cases. A new AI algorithm could help detect Alzheimer's disease early. A new report explains why a number of factors determine whether you need cloud or on-premise solutions for AI and HPC.