Deep learning in fetal, infant, and toddler neuroimaging research.
Authors
Affiliations (11)
Affiliations (11)
- Department of Psychology, University of Denver, Denver, CO, United States.
- Department of Computer Science, University of Copenhagen, Denmark. Electronic address: [email protected].
- Department of Psychology and Neuroscience, Temple University, PA, United States; Department of Psychology, University of Pennsylvania, PA, United States.
- Department of Neuroscience, American University, Washington, DC, United States.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States.
- Department of Psychology, Stanford University, Stanford, United States.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China; Department of Radiology, Harvard Medical School, Boston, MA, United States.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland; School of Psychology, Trinity College Dublin, Ireland.
- Department of Biomedical Sciences and Biomedical Imaging Research Institute (BIRI), Cedars-Sinai Medical Center, Los Angeles,CA, United States; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, United States; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Pediatric Imaging Research Center, Massachusetts General Hospital, Boston, MA, United States.
- Department of Psychiatry at Washington University School of Medicine, St. Louis, United States.
Abstract
Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analysis has progressed more slowly due to high clinical stakes, limited availability of labeled data, and substantial variability in imaging protocols and population. These challenges are further pronounced in the field of fetal, infant, and toddler (FIT) neuroimaging, where datasets are especially scarce and subject to large amounts of anatomical variability. However, deep learning (DL), a specific method within machine learning, which is itself a subfield of AI, has emerged as a powerful framework to adapt to the challenges of medical image analysis. This review is written for the broad FIT research community, including clinicians, neuroscientists, and develop mental scientists who may not have formal training in AI. To make the material accessible, we provide a concise overview of DL concepts before reviewing a selected, and non-exhaustive, list of applications of DL in FIT neuroimaging, including structural image analysis, enhancement of data acquisition, modeling of cognitive and perceptual processes, and automated video tagging. In closing, we discuss best practices for data curation, ongoing challenges, and opportunities for future research.