Several recent discoveries show that the accuracy of diagnosing coronary artery disease and predicting patient risk is improved with the help of artificial intelligence (AI) models developed by scientists in the Division of Artificial Intelligence in Medicine at Cedars-Sinai.
The first study, published in The Journal of Nuclear Medicine, uses AI technology for heart imaging that helps improve the diagnostic accuracy of SPECT imaging for coronary artery disease by advanced image corrections.
Researchers developed a deep-learning model called DeepAC to generate corrected SPECT images without the need of expensive hybrid scanners. These images are generated by AI techniques similar to those used to generate “deep-fake” videos and are able to simulate high-quality images obtained by hybrid SPECT/CT scanners.
The team compared the diagnosis accuracy of coronary artery disease using the non-corrected SPECT images—which are used in most places today—advanced hybrid SPECT/CT images, and new AI-corrected images in unseen data from centers never used in DeepAC training.
They found that AI created images that were near the same quality and allow similar diagnostic accuracy as the ones obtained with more expensive scanners.
In the second study, published in the Journal of American College of Cardiology: Cardiovascular Imaging, the team demonstrated that deep learning AI makes it possible to predict major adverse cardiac events, such as death and heart attacks, directly from SPECT images.
Investigators trained the AI model using a large multinational database that included five different sites with over 20,000 patient scans. It included images depicting heart perfusion and motion for each patient.
The AI model incorporates visual explanations for the physicians, highlighting the images with the regions that are contributing to high risk of adverse events.
The team then tested the AI model in two separate sites with over 9,000 scans. They found the deep learning model predicted patient risk more accurately than the software programs used currently in the clinic.
The third study, published in the European Journal of Nuclear Medicine and Molecular Imaging, describes how to train an AI system to perform well in all applicable populations—not just the population the system was trained on.
Some AI systems are trained using high-risk patient populations, which can cause systems to overestimate the disease probability. To ensure that the AI model works accurately for all patients and reduce any bias, Slomka and his team trained the AI system using simulated variations of patients. This process, called data augmentation, helps to better reflect the mix of patients expected to undergo the imaging tests.
They found the models that were trained with a balanced mix of patients more accurately predicted the probability of coronary artery disease in women and low-risk patients, which can potentially lead to less invasive testing and more accurate diagnosis in women.
The models also led to lower false positives, suggesting that the system can potentially reduce the number of tests the patient undergoes to rule out the disease.
