An investigation of the diversity of outcomes along with machine learning in the prediction of ischemia using PET cardiac perfusion imaging - Guruprasad, Naipunya (My University, 2024)
Positron emission tomography (PET) is an advanced, non-invasive medical imaging technology that allows for precise internal imaging. The recent progress in PET technology, particularly in cardiovascular applications, has significantly improved our knowledge of heart-related conditions. PET scans offer clear visuals of blood circulation and cardiac functions, playing an important role in detecting various conditions. One such condition is ischemia, a state where a part of the body lacks sufficient blood and oxygen supply, posing serious risks. This can be fatal and hence the detection of this is incredibly essential. For accurate predictions of such conditions, healthcare professionals rely on specialised software dedicated to segmentation and visualization of PET cardiac perfusion images, and one such software is Carimas. The integration of predictive machine learning models with Carimas not only enhances but also confirms the accuracy of diagnostic findings, revolutionizing the way we approach cardiac care. The objective was to have individuals from medical and non-medical backgrounds perform segmentation/analysis on Carimas and to then compare them with one another. Additionally, a pre-trained machine learning model uses the data created on the software to predict whether the patient is ischemic or not. This strategy offers a thorough insight of how segmentation techniques used by people with various backgrounds affect the prediction of ischemia, as well as machine learning models. 55 PET cardiac stress perfusion images from ischemic and non-ischemic patients have been used to create polar maps with radiowater [15O-H2O] labelling. The Myocardial Blood Flow (MBF) data is compared using statistical tests like the Wilcoxon test, Jaccard Index and Dice Coefficient. The pre-trained The Convolutional Neural Network (CNN) model undergoes K-fold validation to verify its performance. Our study aimed to evaluate the accuracy of ischemia prediction derived from manual segmentation performed by individuals with diverse backgrounds indeed gives different MBF data which in turn affects the classification of patients. The CNN model, on the other hand, shows a high area under the characteristic curve value indicating good performance that is independent of the level of expertise of the individuals who created the polar maps.
Keywords: Ischemia, PET Imaging, Carimas, Convolutional neural networks