Estimation of Ventricular and Intracranial Hemorrhage Volumes and Midline Shift on an External Validation Data Set Using a Convolutional Neural Network Algorithm
The described Viz ICH-Plus algorithm performed moderately well at quantifying ICH, BLV volume, and MLS with satisfying spatial overlap of artificial intelligence and manual segmentations. The system demonstrated good predictive power when using predetermined thresholds to estimate clinically significant changes.
Authors
- Gautam Edhayan (ORCID: https://orcid.org/0000-0002-0582-2316)
- Dor Amran
- Sean O’Leary (ORCID: https://orcid.org/0000-0003-3650-705X)
- Marco Geron (ORCID: https://orcid.org/0000-0003-4231-0982)
- Nicholas von Waaden
- Muhammad Zeeshan Memon (ORCID: https://orcid.org/0000-0003-0207-1999)
- Christopher J. Love (ORCID: https://orcid.org/0000-0002-7989-2376)
- Peter Kan (ORCID: https://orcid.org/0000-0001-6649-4128)
- Adam Husain
- Marco Colasurdo (ORCID: https://orcid.org/0000-0002-7823-3088)
- Huanwen Alvin Chen (ORCID: https://orcid.org/0000-0003-2455-748X)
- Roberto Medrano García
- Ariadna Robledo (ORCID: https://orcid.org/0000-0002-7981-6103)
- Hashem Shaltoni
- Keren Ziv
Institutions
- Oregon Health & Science University (US)
- Georgetown University (US)
- MedStar Georgetown University Hospital (US)
- The University of Texas Medical Branch at Galveston (US)
Publication Details
- Journal
- Neurosurgery
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1227/neu.0000000000003455
- Citations
- 3
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 4.17