AI could predict Alzheimer’s disease years before diagnosis

Innovations in artificial intelligence (AI) are being developed that could drastically reduce the time it takes for doctors to diagnose a patient with Alzheimer’s disease.

The Radiology and Biomedical Imaging Department at the University of California in San Francisco has conducted new research that has used self-learning computer software to recognise features in brain scans that are too subtle for human doctors to see.

The results indicated that AI-supported insights could help doctors diagnose the early signs of Alzheimer’s disease in patients up to six years sooner.

Dr Jae Ho Sohn, the study’s co-author said: “Differences in the pattern of glucose uptake in the brain are very subtle.

“People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”

AI could be used to highlight the changes in metabolism in the brain, which could potentially be a major breakthrough in the early diagnosis for the disease. Researchers trained the deep learning algorithm via more than 2,100 positron emission tomography scans from 1,002 patients.

As a test, the algorithm was given 40 scans from 40 patients it had never studied before. It proved to be 100 per cent effective in its ability to diagnose Alzheimer’s disease accurately and quickly.

“We were very pleased with the algorithm’s performance,” Dr Sohn concluded. “It was able to predict every single case that advanced to Alzheimer’s disease.”

Responding to the research, Dr Carol Routledge, from the charity Alzheimer’s Research UK, said: “This study highlights the potential of machine learning to assist with the early detection of diseases like Alzheimer’s, but the findings will need to be confirmed in much larger groups of people before we can properly assess the power of this approach.”

Dr Routledge added: “The changes in brain chemistry that highlight the early signs of Alzheimer’s disease can begin in patients up to 20 years prior to the full onset of this debilitating condition.

“It is therefore highly beneficial to be able to detect these changes much sooner, ultimately helping to deliver improved treatment options and outcomes for patients.”

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