J Stroke.  2023 Sep;25(3):425-429. 10.5853/jos.2023.02145.

Acute Infarct Segmentation on Diffusion-Weighted Imaging Using Deep Learning Algorithm and RAPID MRI

Affiliations
  • 1Artificial Intelligence Research Center, JLK Inc., Seoul, Korea
  • 2Department of Neurology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
  • 3Department of Neurology and Cerebrovascular Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea


Figure

  • Figure 1. Comparison of software-estimated and manually segmented infarct volumes. (A) Ground truth infarct volume by manual segmentation vs. estimated infarct volume by RAPID MRI (red) and JBS-01K (blue). The right lower figure enlarged the gray shaded (<10 mL) area. Black dotted lines indicate lesion volume cutoffs of 21, 31, and 51 mL. Green dotted line and shared areas indicate lesion volume cutoff of 70 mL. The threshold of 21, 31, or 51 mL was used in the DAWN trial (DWI or CTP Assessment With Clinical Mismatch in the Triage of Wake-Up and Late Presenting Strokes Undergoing Neurointervention With Trevo) for inclusion of patients, whereas the 70 mL threshold was used in the DEFUSE-3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke) and EXTEND-IA (Extending the Time for Thrombolysis in Emergency Neurological Deficits With Intra-Arterial Therapy) trials. (B) Bland-Altman plot between ground truth infarct volume and estimated infarct volume by JBS-01K. (C) Bland-Altman plot between ground truth infarct volume and estimated infarct volume by RAPID MRI. Green line and black dotted lines indicate mean percentage difference and its 95% limit. Note that if the software did not find infarct, the percent difference was calculated as 200%. DWI, diffusion-weighted imaging; CTP, computed tomography perfusion.


Reference

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