Modern computing methods: harnessing AI, graph theory, and quantum computing

Nowadays, there has been an increasing amount of data available, such as medical records and medical images, which can hold valuable insights if they can be uncovered. Finding appropriate steps to analyze and make sense of all this data, which can be complex, is important.

One approach to this challenge is by using computer techniques that learn from patterns called Machine Learning. In this thesis, machine learning models were used to predict if some elderly people are more likely than others to experience a hospital admission for a fall injury. Patterns in age, sex, comorbidities, and hospital information were analyzed in different ways to provide a ranking of the most impactful factors and how having multiple factors can affect potential outcomes.

In another study, we applied the same computing techniques to examine combinations of life, work, and relationship circumstances that are connected to future suicide deaths by firearms. By combining the predictions of multiple machine learning models, we identified groups of factors that are linked to this type of tragedy in the male population. This information could be used to create tools that help identify people who might be at risk and develop ways to prevent such incidents.

When it comes to medical images, there is a need for automatic tools for the identification of anatomical structures. We develop two approaches to tackle the problem of detecting unruptured brain aneurysms that potentially eliminate the need for manual work. The first method relies on LOGISMOS, a graph-based framework for finding surfaces in 3-D images, that provides an interactive platform allowing for the identification of aneurysm sacs and vessels with few user clicks. The second proposed method utilizes deep learning and the availability of public datasets to construct an automatic segmentation system. The two methods were tested on our in-house datasets provided by the University of Iowa Hospital and Clinics which yielded promising results, indicating the potential for practical application.

Lastly, we explored an emerging technology called quantum computing. Think of it as a unique way of processing information in the world of quantum mechanics. We developed an implementation of LOGISMOS that is solvable in quantum computers. In our simulation studies, the results showed that the quantum version of LOGISMOS is capable of delivering correct solutions for simple segmentation tasks. This is a promising step toward the development of quantum computing methods for medical image analysis.