Grant: Discovery Grants Program - Individual

This research program involves the development of advanced computational technologies in machine learning (ML) and pattern recognition along with novel validation and evaluation techniques applied to large-scale real world medical imaging examinations in pursuit of the next generation of applications in medical diagnostics. The proposed technological and ML evaluation methods will be developed towards the creation of diagnostic and disease characterization technologies that can help improve the standard of patient care for children with neurodevelopmental disorders. Technologies developed will be evaluated with large datasets of MRI examinations of patients with a variety of conditions such as autism, attention deficit hyperactivity disorder (ADHD) and more. This will include the evaluation of proposed novel ML technology on publicly available MRI examinations and the translation of those technologies to applications based on routine clinical imaging exams (Boston Children's Hospital, Harvard Medical School, where I hold an appointment as a Research Associate). This will support larger scale evaluation of whether novel proposed technologies developed as part of this proposal have a potential role to play in a realistic clinical population. The availability of extensive routine clinical imaging examinations supports the validation of created technologies across an assortment of medical disorders (multiple sclerosis, cerebral palsy, neurofibromatosis, schizophrenia and more). With a multidisciplinary technical background (computation, medical physics) and extensive experience in interdisciplinary medical research (neuroscience, biomedical engineering) and many novel research avenues identified, I am uniquely positioned to succeed in this proposed research. This research program will involve the extraction of measurements of interest from large datasets using existing pattern recognition techniques along with the development of novel general purpose ML algorithms and validation approaches extensively assessed on large collections of brain MRI examinations. ML will be employed to combine the measurements extracted by the pattern recognition techniques to improve diagnostics and disorder characterization. This proposal will involve the use of existing techniques, as well as the development of novel ML methods that build on the pre-existing contributions of my work, including formulations which allow for a flexible decision boundary that varies with the test bias setting, providing optimizations for diagnostic testing. Rigorous statistical validation techniques will be employed and results will be confirmed with independent datasets wherever possible (autism, ADHD, etc.). Novel validation techniques will be developed to assess sample size and error consistency issues. These validation techniques will be general-purpose and thus can be used by all ML application developers.