Machine learning is contributing to rapid advances in clinical translational imaging to enable early detection, prediction, and treatment of diseases that threaten brain health.

Several faculty members, researchers and students affiliated with the Harvard-MIT Health Sciences and Technology (HST) program, and with the MIT Institute for Medical Engineering and Science (IMES), wrote the following report on machine learning and brain health. They include Nalini M. Singh, Jordan B. Harrod, Sandya Subramanian and Mitchell Robinson, all HST Medical Engineering and Medical Physics (MEMP) students; Ken Chang, Adrian Vasile Dalca and Lauren J. O’Donnell, all HST alums; M. Brandon Westover and Randy Gollub, both HST faculty, and Polina Golland, an affiliate faculty member at IMES. IMES is HST’s home at MIT.

Abstract

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function – Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

To read the report, go to the link at Neuroinformatics.