Thursday January 23, 2020

AI Can Give More Accurate Results for Cardiac MRI

Utilising AI, a scan can be analyzed with comparable precision in approximately four seconds

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Based on the number of scans per year, researchers believe that utilising AI to read scans could potentially lead to saving 54 clinician days per year. Pixabay

Cardiac magnetic resonance imaging (MRI) analysis can be performed significantly faster with precision similar to experts when using artificial intelligence also Known as AI in the form of automated machine learning, according to a new study.

Currently, analysing heart function on cardiac MRI scans takes approximately 13 minutes for humans.

Utilising AI, a scan can be analyzed with comparable precision in approximately four seconds, according to the findings published in the journal Cardiovascular Imaging.

“Our dataset of patients with a range of heart diseases who received scans enabled us to demonstrate that the greatest sources of measurement error arise from human factors.

“This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘super-human’–transforming clinical and research measurement precision,” said study author Charlotte Manisty from the University College London.

In the UK, where the study was conducted, it is estimated that more than 150,000 cardiac MRI scans are performed each year.

Based on the number of scans per year, researchers believe that utilising AI to read scans could potentially lead to saving 54 clinician days per year at each UK health centre.

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Cardiac magnetic resonance imaging (MRI) analysis can be performed significantly faster with precision similar to experts when using artificial intelligence also Known as AI in the form of automated machine learning, according to a new study. Pixabay

Researchers trained a neural network to read the cardiac MRI scans and the results of almost 600 patients.

However, when the AI was tested for precision compared to an expert and trainee on 110 separate patients from multiple centres, researchers found that there was no significant difference in accuracy.

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This study highlights the potential that AI techniques could have in the future to improve analysis and influence clinical decision-making for patients with heart disease. (IANS)

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This AI can Detect Low-Sugar Levels Without any Fingerprick Tests

AI can spot low-glucose levels without fingerprick test

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Current methods to measure sugarrequires needles and repeated fingerpicks over the day. Pixabay

Researchers have developed a new Artificial Intelligence (AI)-based technique that can detect low-sugar levels from raw ECG signals via wearable sensors without any fingerprint test.

Current methods to measure glucose requires needles and repeated fingerpicks over the day. Fingerpicks can often be painful, deterring patient compliance.

The new technique developed by researchers at University of Warwick works with an 82 per cent reliability, and could replace the need for invasive finger-prick testing with a needle, especially for kids who are afraid of those.

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Fingerpicks are never pleasant for a sugar-level test and in some circumstances are particularly cumbersome. Pixabay

“Our innovation consisted in using AI for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping,” said Dr Leandro Pecchia from School of Engineering in a paper published in the Nature Springer journal Scientific Reports.

Two pilot studies with healthy volunteers found the average sensitivity and specificity approximately 82 per cent for hypoglycaemia detection.

“Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in paediatric age,” said Pecchia.

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The figure shows the output of the algorithms over the time: the green line represents normal glucose levels, while the red line represents the low glucose levels.

“Our approach enable personalised tuning of detection algorithms and emphasise how hypoglycaemic events affect ECG in individuals. Basing on this information, clinicians can adapt the therapy to each individual,” the authors wrote. (IANS)