How Machine Learning with CT Scans Can Help Better Cardiovascular Diagnosis

by | Aug 4, 2021 | CT

Reading Time: 2 minutes

Major coronary events are difficult to see and predict, but thankfully, advancements in technology have paved the way for healthcare professionals to spot them easily. The combination of machine learning with CT can help a doctor’s ability to predict a patient’s risk of adverse events in the future. 


The Help of Machine Learning

Machine learning can improve the management and care of patients who are at risk for serious heart conditions. With it, the process of integrating patients’ clinical information can be improved, improving the quality of their risk assessment. 

It works through algorithms that detect patterns associated with diseases and health conditions based on healthcare records and other patient data. In fact, the recent developments in machine learning help increase healthcare access in developing countries. 

CT scans, MRIs, and other imaging technologies—despite their high-resolution detail—can still be improved with the help of machine learning. Over the years, machine learning has shown its value in helping healthcare professionals to improve their precision and productivity. 

Some of the common cases where machine-learning is used along with imaging technologies are cardiovascular abnormalities, musculoskeletal injuries, and cancer screening. 

Moreover, machine learning uses real-time data, as well as medical records and information gathered from previous successful surgeries. It reduces human error, aids with more complex procedures, and improves the implementation of less invasive surgeries. 


Machine Learning & CT For Cardiovascular Conditions

Machine learning has assisted with the calcium quantification algorithm of the chest and cardiac CTs. This is illustrated in a study that included 95 patients who underwent dedicated cardiac and chest CT, with a median number of days between the two exams of 186. They were compared with a control group of 168 people who had chest CT qualitative calcium classification only. It became clear that the AI algorithm performed to a superior standard compared with other modes of assessment. 

Moreover, machine-learning provides a time-efficient way to evaluate clinical data, specifically coronary plaque burden by location and extent and the severity of the coronary artery disease. Without machine learning, the process is time-consuming. 

Another study was conducted on 361 patients with suspected coronary artery disease who went through cCTA. All major adverse cardiac events that happened within 90 days after the exam was noted. One team used a machine-learning algorithm trained in cCTA-derived plaque measures, cardiovascular risk factors to predict adverse outcomes. Its performance was measured based on the receiver operating characteristic curve (AUC). The algorithm showed higher predictive power compared to conventional risk scores, plaque measures, and regression analysis. 


Final Thoughts

Machine-learning combined with CT can significantly improve the accuracy of cardiovascular risk prediction. In turn, this helps increase the number of patients identified who can benefit from preventive treatment. Deep-learning algorithms can cut the time to review patient and medical data, which will lead to faster diagnosis and quicker recovery. 

With that, a growing number of healthcare organizations are integrating machine learning into their processes. AI combined with CT scans show promising results that allow for better imaging, which will lead to better treatments in the future. 

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