Neurological Diseases

  • Nathaniel SwinburneEmail author
  • Andrei Holodny


Neuroimaging represents an intriguing target for AI applications for several reasons including the high morbidity and mortality associated with neurological diseases. Technical challenges remain due to the volumetric and multiparametric nature of neuroradiological imaging; however advances in GPU power and development of novel deep learning architectures continue the field’s advancement. AI applications to neuroimaging have shown success at handling a range of tasks involving all stages from an imaging study’s acquisition through its interpretation. Preprocessing steps commonly performed before utilization in AI applications include skull stripping, normalization, and coregistration. AI has demonstrated the ability to perform study protocoling; shorten image acquisition times of conventional, DTI, and ASL MRI; and generate synthetic images using a different imaging modality. The use of AI to perform tissue and lesion (e.g., tumor and MS plaque) segmentation is an area of active research. Newer applications have shown success at identification and quantification of specific disease processes including infarcts, tumors, and intracranial hemorrhage, as well as more robust approaches that surveil for multiple acute neurological diseases.


Neuroimaging Neuroradiology Brain MRI CT Stroke Tumor Intracranial hemorrhage Machine learning Segmentation 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Neuroradiology Service, Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkUSA

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