The application of the tricks of the astronomical trade to other contemporary problems, particularly problems in the biomedical sciences, has been something I have been working on from the time of my Ph.D. studies. The scale, modalities and complexity of the underlying biological data provide interesting opportunities to explore novel approaches in imaging and artificial intelligence - as long as you have a reasonable understanding of the underlying biology! I spent five years as a genetics professor in one of New York City’s medical schools, and whilst there, honed my specific interests to the intersection between physics, genomics, radiation oncology and imaging.
Expediting the diagnosis of COVID-19 in a clinical setting using AI enabled analysis of CT scans
COVID-19 associated pneumonia is evident in chest CT scans when examined by specialist radiologists, offering a diagnostic route for suspected patients with negative RT-PCR. Can AI enabled radiomic analysis both expedite and support this process, in particular for early stage disease that are difficult to discern in high risk patients? This question forms the basis for a proposal submitted under the Health Research Board’s COVID-19 Pandemic Rapid Response Funding Call (COV19 2020) that was subsequently funded. Our project aims to build an AI imaging system to support radiology teams in the diagnosis of COVID-19 disease using CT scans - our turn-key system will be trained on thousands of different types of archival image data and when used to examine a suspected COVID-19 patient’s CT scan, will be able to screen and report ‘actionable’ lesions in minutes, expediting the radiology teams’ ability to identify the subtle differences between COVID-19 disease and other more common lung disorders, particularly in patients for whom immediate attention is critical. This project is a collaboration with Dr. Christoph Kleefeld (Medical Physics & Clinical Engineering, University Hospital Galway) and Dr. Declan Sheppard (Clincal Director Radiology, University Hospital Galway)
Radiogenomics & Precision Radiotherapy
The ability to molecularly profile tissues from a patient provides a powerful means of identifying biomarkers that can be used either determine the likelihood of that patient suffering from excessive toxicity as a consequence of radiotherapy, or indeed, assess if an individual might benefit from radiotherapy over other forms of treatment for cancer. Recent work in defining radio sensitivity indices (RSI) that combine gene expression profiles with radiobiological concepts such as the Linear Quadratic model of cell survival against radiation dose is opening up ways in which we can more effectively model radiotherapeutic response. Similarly, combining radiological data with both genomic and clinical data from the same patient offers a new biomarker descriptor space that is showing promise in the area of precision medicine.
This figure shows preliminary work conducted by Brian O’Sullivan whilst completing his M.Sc. in Computational Genomics.