creator Obioma, Blessing Ngozi 2020 Spring 2020 Thesis Illinois Institute of Technology BME / Biomedical Engineering advisor Kawaji, Keigo Artificial intelligence Medical imaging Biomedical engineering Artificial Intelligence Convolutional Neural Network Deep Learning multicenter reproducibility Myocardial strain measurement SENC MRI en Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time. born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access http://hdl.handle.net/10560/islandora:1025011