AI tool improves early heart disease detection
A new artificial intelligence model has outperformed existing methods in detecting cardiac amyloidosis, a progressive heart condition that is often missed in its early stages.
The tool analyses routine ultrasound images of the heart and was found to identify the disease with 85 per cent accuracy, while ruling it out with 93 per cent accuracy.
Researchers say this could support earlier diagnosis and allow more patients to benefit from recently developed treatments.
Cardiac amyloidosis occurs when abnormal proteins build up in the heart muscle, causing it to stiffen and impairing its ability to pump blood.
Although life-prolonging drug therapies have recently become available, the condition is frequently misdiagnosed because it resembles other cardiac disorders and often requires extensive testing to confirm.
The AI model was developed by researchers at the Mayo Clinic and Ultromics Ltd, who trained a neural network using thousands of echocardiograms – ultrasound videos showing the heart in motion.
The tool analyses a single video view of the heart’s apical four chambers to detect signs of cardiac amyloidosis and distinguish it from similar conditions.
Dr Jeremy Slivnick, co-lead author and cardiologist at the University of Chicago Medicine, said: “Cardiac amyloidosis can be challenging to diagnose, because it’s often difficult to distinguish from other heart issues without a burdensome amount of testing.”
The University of Chicago Medicine joined 17 other hospitals worldwide in validating the algorithm across a large and ethnically diverse population.
The model demonstrated high accuracy across multiple subtypes of cardiac amyloidosis.
In their analysis, researchers compared the AI model with clinical scoring methods currently used to detect the disease and found it significantly outperformed traditional tools.
They say this could help doctors decide which patients should be referred for advanced imaging or further evaluation.
Slivnick said: “It was exciting to confirm that artificial intelligence can give clinicians reliable information to augment their expert decision-making process.
“Since the new treatments for cardiac amyloidosis are most effective in early stages of the disease, it’s critical that we leverage every tool at our disposal to diagnose it as soon as possible.”
The model has received clearance from the US Food and Drug Administration and is already being implemented in several hospitals.
Because it analyses a standard echocardiographic view routinely captured during heart scans, it can be integrated into existing clinical workflows without requiring extra procedures or equipment.
Slivnick said: “This AI model provides a practical solution.
“Because it automatically analyses a common echocardiogram view, it can easily integrate into everyday clinical practice without causing hassle or sacrificing diagnostic accuracy.”
Researchers say the tool could improve early diagnosis of cardiac amyloidosis in older adults, who are at higher risk.
Earlier detection could enable timely access to treatment that helps extend survival and maintain quality of life.
