Did you know that when a patient enters an emergency room and tells the doctor that he is having a heart attack, the doctor relies on a series of diagnostic questions to determine whether the patient is actually dealing with cardiac troubles? Generally speaking, the doctor will ask the patient if he is suffering from tingling in the arms, is dealing with shortness of breath, and is having chest pain. From there, depending on what the patient describes, a doctor will recommend that the patient quickly receive a CT angiogram to further determine if the patient is undergoing cardiac arrest.
To some, this observation-based approach to heart attack identification seems rudimentary and time-consuming. You are probably asking yourself: “With all of the cutting-edge medical technology out there, a doctor still has to rely on a set of diagnostic questions to determine whether someone is having a heart attack?”
Another glaring problem with this observation-oriented approach is that some patients suffer from none of these symptoms when they are in the midst of a heart attack, while other patients exhibit all of the aforementioned symptoms. In addition to that, sometimes a CT angiogram scan is even unable to detect the presence of a heart attack!
This is what makes treating and preventing heart disease so difficult.
Thankfully, new advancements in medical technology — particularly in the machine learning space and artificial intelligence sector — are helping doctors find innovative ways to detect heart attacks that rely more on hard scientific data and less on looking for particular symptoms.
Rapid Blood Draw Technology Identifies Heart Attacks
A comprehensive study conducted in Australia examining over 11,000 patients suggests that an innovative machine learning technology will help doctors detect heart attack risk in patients quicker than ever before.
The study, conducted in-part by Dr. Louise Cullen at the Royal Brisbane and Women’s Hospital, used rapid blood reading technology to test for proteins like troponin in the blood. According to Dr. Cullen, the presence of elevated levels of troponin in the blood indicates that a patient has suffered heart damage and might be having a heart attack.
More Improvement in Machine Learning Needed
Dr. Cullen argues that using immediate blood draw technology is an excellent way to quickly treat at-risk heart attack patients. However, she says the technology still isn’t perfect. At the moment, this machine learning technology only examines troponin levels on a broad level, and does not take into account a patient’s gender, medical history, or age.
Dr. Cullen believes that more specificity is needed for blood draw machine learning technology to truly revolutionize how doctors treat cardiac arrest.