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Whenever a patient gets a CT scan at the University of Texas Medical Branch (UTMB), the resulting images are automatically sent off to the cardiology department, analyzed by AI and assigned a cardiac risk score.
In just a few months, thanks to a simple algorithm, AI has flagged several patients at high cardiovascular risk. The CT scan doesn’t have to be related to the heart; the patient doesn’t have to have heart problems. Every scan automatically triggers an evaluation.
It is straightforward preventative care enabled by AI, allowing the medical facility to finally start utilizing their vast amounts of data.
“The data is just sitting out there,” Peter McCaffrey, UTMB’s chief AI officer, told VentureBeat. “What I love about this is that AI doesn’t have to do anything superhuman. It’s performing a low intellect task, but at very high volume, and that still provides a lot of value, because we’re constantly finding things that we miss.”
He acknowledged, “We know we miss stuff. Before, we just didn’t have the tools to go back and find it.”
How AI helps UTMB determine cardiovascular risk
Like many healthcare facilities, UTMB is applying AI across a number of areas. One of its first use cases is cardiac risk screening. Models have been trained to scan for incidental coronary artery calcification (iCAC), a strong predictor of cardiovascular risk. The goal is to identify patients susceptible to heart disease who may have otherwise been overlooked because they exhibit no obvious symptoms, McCaffrey explained.
Through the screening program, every CT scan completed at the facility is automatically analyzed using AI to detect coronary calcification. The scan doesn’t have to have anything to do with cardiology; it could be ordered due to a spinal fracture or an abnormal lung nodule.
The scans are fed into an image-based convolutional neural network (CNN) that calculates an Agatston score, which represents the accumulation of plaque in the patient’s arteries. Typically, this would be calculated by a human radiologist, McCaffrey explained.
From there, the AI allocates patients with an iCAC score at or above 100 into three ‘risk tiers’ based on additional information (such as whether they are on a statin or have ever had a visit with a cardiolog …