Echocardiography has been the preferred imaging modality to study the heart chambers for routine screening purposes. However, it suffers from subjective interpretation and inter- and intra-operator variability. The clinical need for fast, accurate and automated analysis of echocardiograms is currently unmet.

Precise automatic analysis will offer considerable improvements in clinical workflow efficiency and reproducibility. Automatic segmentation and tracking of cardiac chambers are the essential steps for consistent calculation of clinical indices such as ventricular volumes and ejection fraction. Currently, segmentation of echocardiographic images is a manual and tedious task. Machine learning has recently been the center of attention in radiology helping automatic identification of complex patterns to make clinical intelligent decisions. My contributions in this thesis to the field of medical imaging will embrace: (1) Fully automated approach for segmentation of heart chambers in echocardiography images, based on discriminative deep-learning algorithms. (2) Improving segmentation performance by taking advantage of adversarial training. (3) Fully automated tracking of all heart chambers in cardiac cycles using semi-supervised deep learning methods.

The functionality of our approaches is evaluated using a dataset of 1000 annotated images from 100 normal subjects’ echocardiography records. Using several measures of errors, the degree of similarity between the manual and automatic segmentation was defined. Our promising results corroborate that deep-learning algorithms can be successfully employed to solve the challenging problem of automatic echocardiography analysis.

There is a need for an oral irrigation device for reducing odor causing bacteria built up in the throat, preventing tonsil stone formation, and clearing existing tonsil stones. This thesis examined the efficacy of novel shower heads, a deep oral irrigation device, and an oral rinse for cleaning the tonsillar crypts. The custom shower heads tailored for reaching the tonsils were examined with an existing oral irrigation device. Once an effective shower head has been finalized, a deep oral irrigation and tonsil cleaning device was developed based on user requirements from patients affected by halitosis. The novel device has demonstrated the ability to reach the tonsillar fossa behind the tonsillar pillar, dislodge trapped tonsil stones, and rinse the area without spilling liquid back onto the hand of the user. A zinc-based mouth rinse was examined when conducting a minimal inhibitory concentration experiment against S. aureus, a model oral pathogen responsible for a wide variety of diseases including tonsillitis. The mouth rinse was effective at inhibiting S. aureus growth with a minimum inhibitory concentration for 50% growth (MIC50) of 10% (v/v). Both the mouthwash itself and zinc acetate were successful in inhibiting the growth of S. aureus. A minimum inhibitory concentration for 90% growth (MIC90) was achieved at 100 µg/mL which did not change in 10% mouthwash. Overall, the combination of mouth rinse and zinc acetate has shown promising antibacterial activity with great potential to be used as an oral wash for future studies.

Current treatment options for advanced heart failure patients are either heart transplantation or mechanical heart assist device systems with their own advantages and disadvantages. This thesis examined the use of helically structured design for novel heart assist device which is inspired by the heart`s own intrinsic helical muscle fiber structure. The study device utilizes a novel external system that assists the heart`s natural helical motion outside the pericardium without direct blood contact. The proof of concept of the helically inspired assist device is also examined on the bench and ex vivo studies. The test VAD was verified twice on bench testing using an excised pig heart having moderate rigor mortis. A cradle was constructed for us to put the dissected pig heart on top of it and placed our pump around the heart. The pump is rotated by an attached motor to lower ring, which was controlled with an Arduino system. A flow tube was attached to the output of the pig heart aorta to observe volume changes through it. In these bench tests, although not a fresh harvest, a dissected pig heart was tested; we were able to observe how the pump helps contract the ventricle. An early prototype, which has no adjustable upper ring, shows slight damage onto the endocardial heart muscle whereas the latest design where the upper ring can be adjustable showed none. The helically fashioned design exhibits promising candidate for future of the heart assist device technology and the people who suffer from advanced heart failure.

Current advances in both deep learning techniques and in cloud computing allow the advancement of innovations that work to the benefit of physicians and patients. This dissertation explores leveraging of these advancements to create a cloud-based analysis platforms for physicians to analyze cardiac MRI as well as a four-tier outcome prediction machine learning model for COVID-19 patients based on their chest X-rays and metadata. The MRI analysis website is hosted on the American Heart Association’s (AHA) Precision Medicine Platform (PMP) and integrates the cardiac MRI segmentation model by Karimi-Bidhendi, et al.2 The back-end web framework was created using Python and Django, with MySQL as the database manager. This allowed a flexible and reliable base to build the website on as well as strong support from the AHA. The website includes an automatic end-systolic (ES) and end-diastolic (ED) detection system for each ventricle, which allows physicians to upload patients’ MRI DICOMs without the need to manually select files relating to each cardiac phase for each ventricle. Hundreds of files are processed in seconds and a report of all segmented images relating to the ED and ES phases for each ventricle as well as the associated ventricle volumes would be immediately presented after file processing. With regards to the COVID-19 outcome prediction model, 6,259 chest X-ray images from 1,771 patients seen at UCI and UCLA Medical Centers were used to train two VGG16 models and a CheXNet model. The first VGG16 model is a convolutional neural network (CNN) that processed only the chest X-ray images and the second is a CNN for the images as well as a separate deep neural network (DNN) for patient metadata including age and BMI and another DNN that processes the combined output of the CNN and the metadata DNN. This combination allows both images and metadata to be factored in when training the model. The CheXNet model is tailored specifically for chest X-ray images and was used to assess the performance of the VGG-16 models. The accuracy of the image-only VGG16 model was 56% on the four-class prediction, compared to 59% for the image and metadata VGG16 model. The CheXNet model resulted in 60% accuracy. This suggests that the metadata did not significantly improve the performance of the model and that the image data was not informative enough beyond 60% accuracy for four-tier predicting of COVID-19 patients’ outcomes.