How AI can predict heart attacks and stroke
Researchers have used Artificial Intelligence (AI) for the first time to instantly and accurately measure blood flow.
The results were found to be able to predict chances of death, heart attack and stroke, and can be used by doctors to help recommend treatments which could improve a patient's blood flow, according to the study, published in the journal Circulation.
"We have tried to measure blood flow manually before, but it is tedious and time-consuming, taking doctors away from where they are needed most, with their patients," said study researcher James Moon from University College London in the UK.
Heart disease is the leading global cause of death and illness. Reduced blood flow, which is often treatable, is a common symptom of many heart conditions.
International guidelines therefore recommend a number of assessments to measure a patient's blood flow, but many are invasive and carry a risk.
Non-invasive blood flow assessments are available, including Cardiovascular Magnetic Resonance (CMR) imaging, but up until now, the scan images have been incredibly difficult to analyse in a manner precise enough to deliver a prognosis or recommend treatment.
In the largest study of its kind, researchers took routine CMR scans from more than 1,000 patients and used a new automated artificial intelligence technique to analyse the images.
By doing this, the teams were able to precisely and instantaneously quantify the blood flow to the heart muscle and deliver the measurements to the medical teams treating the patients.
By comparing the AI-generated blood flow results with the health outcomes of each patient, the team found that the patients with reduced blood flow were more likely to have adverse health outcomes including death, heart attack, stroke and heart failure.
The AI technique was therefore shown for the first time to be able to predict which patients might die or suffer major adverse events, better than a doctor could on their own with traditional approaches.
"The predictive power and reliability of the AI was impressive and easy to implement within a patient's routine care," said study researcher Kristopher Knott.
"This study demonstrates the growing potential of artificial intelligence-assisted imaging technology to improve the detection of heart disease and may move clinicians closer to a precision medicine approach to optimize patient care," said Peter Kellman, who developed the automated AI techniques to analyse the images that were used in the study.
The results were found to be able to predict chances of death, heart attack and stroke, and can be used by doctors to help recommend treatments which could improve a patient's blood flow, according to the study, published in the journal Circulation.
"We have tried to measure blood flow manually before, but it is tedious and time-consuming, taking doctors away from where they are needed most, with their patients," said study researcher James Moon from University College London in the UK.
Heart disease is the leading global cause of death and illness. Reduced blood flow, which is often treatable, is a common symptom of many heart conditions.
International guidelines therefore recommend a number of assessments to measure a patient's blood flow, but many are invasive and carry a risk.
Non-invasive blood flow assessments are available, including Cardiovascular Magnetic Resonance (CMR) imaging, but up until now, the scan images have been incredibly difficult to analyse in a manner precise enough to deliver a prognosis or recommend treatment.
In the largest study of its kind, researchers took routine CMR scans from more than 1,000 patients and used a new automated artificial intelligence technique to analyse the images.
By doing this, the teams were able to precisely and instantaneously quantify the blood flow to the heart muscle and deliver the measurements to the medical teams treating the patients.
By comparing the AI-generated blood flow results with the health outcomes of each patient, the team found that the patients with reduced blood flow were more likely to have adverse health outcomes including death, heart attack, stroke and heart failure.
The AI technique was therefore shown for the first time to be able to predict which patients might die or suffer major adverse events, better than a doctor could on their own with traditional approaches.
"The predictive power and reliability of the AI was impressive and easy to implement within a patient's routine care," said study researcher Kristopher Knott.
"This study demonstrates the growing potential of artificial intelligence-assisted imaging technology to improve the detection of heart disease and may move clinicians closer to a precision medicine approach to optimize patient care," said Peter Kellman, who developed the automated AI techniques to analyse the images that were used in the study.