Sunday September 22, 2019

Novel AI Tool May help to Predict Alzheimer’s risk

Globally, around 50 million people have dementia and the total number is projected to reach 82 million in 2030 and 152 in 2050, according to the World Health Organization

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Poor sleep can predict Alzheimer's Risk in elderly. Pixabay

A team of scientists, including one of an Indian-origin, has successfully trained a new Artificial Intelligence (AI) algorithm that may soon help doctors to make accurate predictions regarding cognitive decline leading to Alzheimer’s disease and provide intervention.

The team, from the McGill University in Canada, designed an algorithm that learns signatures from magnetic resonance imaging (MRI), genetics, and clinical data.

This specific algorithm can help predict whether an individual’s cognitive faculties are likely to deteriorate towards Alzheimer’s in the next five years.

“At the moment, there are limited ways to treat Alzheimer’s and the best evidence we have is for prevention. Our AI methodology could have significant implications as a ‘doctor’s assistant’ that would help stream people onto the right pathway for treatment,” Mallar Chakravarty, assistant professor at the University’s Department of Psychiatry.

“For example, one could even initiate lifestyle changes that may delay the beginning stages of Alzheimer’s or even prevent it altogether,” she added.

Alzheimer's
In Alzheimer’s disease, patients start losing memory, Pixabay

For the study, published in the journal PLOS Computational Biology, the team trained their algorithms using data from more than 800 people ranging from normal healthy seniors to those experiencing mild cognitive impairment, and Alzheimer’s disease patients.

“We are currently working on testing the accuracy of predictions using new data. It will help us to refine predictions and determine if we can predict even farther into the future,” Chakravarty noted.

With more data, doctors would be able to better identify those in the population at greatest risk for cognitive decline leading to Alzheimer’s.

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Globally, around 50 million people have dementia and the total number is projected to reach 82 million in 2030 and 152 in 2050, according to the World Health Organization.

Alzheimer’s disease, the most common form of dementia, may contribute to 60-70% of cases. Presently, there is no truly effective treatment for this disease. (IANS)

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Researchers Can Now Detect Heart Failure with 100% Accuracy with the Help of Artificial Intelligence

Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy

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Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

With the help of Artificial Intelligence(AI), researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 per cent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.

The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data.

“We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100 per cent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure,” said study researcher Sebastiano Massaro, Associate Professor at the University of Surrey in the UK.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“Our model is also one of the first known to be able to identify the ECG’ s morphological features specifically associated to the severity of the condition,” Massaro said.

Published in Biomedical Signal Processing and Control Journal, the research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors.

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Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy.

“With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology,” said study researcher Leandro Pecchia from the University of Warwick. (IANS)