Applied artificial intelligence for medical imaging

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Motivation. Artificial intelligence (AI) is promising tool to advance medical practice. Adapting widely used approaches such as convolutional neural networks to play a significant role in clinical support is challenging and, in many cases, it will be application specific. My research program seeks to design, develop, and deploy AI techniques to assist medical personnel in the detection and diagnosis of different neurological conditions. Methods. I propose three interconnected projects that will advance the application of artificial intelligence in the field of neuroimaging. In Project 1, we will improve algorithms for neurodegenerative diseases classification using deep learning. Currently, the standard approach is to use an existing deep learning architecture trained on a large natural image dataset, and then fine--tune it to the medical data. Due to the differences between natural images and neurological data, this approach does not lead to optimal results. We will use simpler convolutional architectures trained directly on the neurological data. The outcome of this project will be a MRI-specific neural network that will then be applicable to other neuroimaging data sets via transfer learning. Project 2, This project involves the design of novel computer vision algorithms to identify brain tumors. We will focus on gliomas, a type of tumor that usually appear diffused in MRI images making their segmentation a challenging task. The goal of this project is to create software that can identify gliomas and provide information regarding their location, size, and type, as well as quantification of the tumor change in longitudinal data. The system will aid the medical professional to identify specific changes in the tumor, avoiding the current subjective and error-prone visual inspection. Project 3 will advance the algorithms for longitudinal analysis of neonatal MRI data by creating a brain-growth trajectory model from neonate to adulthood. I will extend my previous work in neonatal imaging by creating brain templates from each year of life from 1 to 80 years of age to create the MRI developmental toolbox. This project will provide a software toolbox for clinical research that will include multi-age templates, registration, and modeling functions. This currently non-existent toolbox will help to identify degeneration trajectories providing relevant information for drug design and treatment evaluation. Impact. The research in this proposal will improve the applicability of AI in medical imaging, acting as a bridge between computer science and the health system. As co-director of the ENIGMA-Ataxia group, I will distribute our software to be tested and adopted in the hospitals from this project (21 sites across the world). Our software will reduce the workload of specialized professionals making neurological diagnoses faster and more accurate. Hence, improving the quality of treatment that patients will receive from the health-care system.