This study aims to diagnose objectively term neonate hypoxic ischemic encephalopathy (HIE) by using deep learning network to extract deep information from multiple modalities of conventional magnetic resonance (MR) images. Neonate HIE diagnosis accuracy is restricted to lesion diversity, MR images quality, high interobserver variability. The network got high diagnosis accuracy in the ROC curve. The network could detect severe neonate HIE with characteristic appearance such as basal ganglia injury and periventricular leukomalacia. The network can help diagnosis neonate HIE objectively without the effect of different radiologist experience and contribute to risk stratification and clinical decision making.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords