Jenna Chin, Madeleine K. Wyburd, Vladislav Ayzenberg, Laurie Bayet, Berkin Bilgiç, Emily M. Chen, Yuting Chen, Áine T Dineen, Shohei Fujita, Janelle Liu, Yohan Jun, M. Catalina Camacho, Lilla Zöllei
Our findings suggest improvement in image quality with the use of the deep learning reconstruction algorithm in 3D T1-weighted neonatal brain MRI, though the small sample size and pilot design limit generalizability. Diagnostic accuracy and clinical outcomes were not evaluated and warrant future investigation.
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.