Abstract #1909

A Deep Transfer Learning Model to Predict Patient Outcome in ICH using the Fusion of Clinical and Fluid-Attenuated Inversion Recovery Imaging Data

Jinghua Wang¹, Ming Chen^2,3, Lili He^2,4, Hailong Li², Vivek Khandwala¹, David Wang¹, Brady Williamson¹, Daniel Woo⁵, and Achala Vagal¹

¹Radiology, University of Cincinnati, Cincinnati, OH, United States, ²The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, ³Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, United States, ⁴Pediatrics, University of Cincinnati, Cincinnati, OH, United States, ⁵Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, OH, United States

Timely and reliable prognostic tools for intracerebral hemorrhage (ICH) have great potential to guide physician decision making. They are potentially useful for targeting patients for interventions and optimizing rehabilitation strategies. The objective of this study is to investigate if a deep transfer learning model can capture individual variability to predict clinical outcome for ICH patients at 3 months using the integration of clinical and T₂-weighted fluid-attenuated inversion recovery (FLAIR) imaging data. Our model was able to correctly identify patients likely to have unfavorable outcomes with an AUC of 0.87 (95% confidence interval: 0.86, 0.89).

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