Automated deep learning pipeline for callosal angle quantification
These results indicate that our fully automated CA measurement framework is a reliable and reproducible alternative to manual methods, outperforms reported interobserver variability in assessing the CA, and offers significant potential to enhance early detection and diagnosis of NPH in both research and clinical settings.
Authors
- Siavash Shirzadeh Barough (ORCID: https://orcid.org/0000-0002-3903-8343)
- Carlo Ventura (ORCID: https://orcid.org/0000-0001-9333-0321)
- M. Luciano (ORCID: https://orcid.org/0000-0002-5664-2956)
- Murat Bilgel (ORCID: https://orcid.org/0000-0001-5042-7422)
- Michael I. Miller
- Marilyn S. Albert
- Abhay Moghekar
- Lucas An
- Ameya Moghekar
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- National Institute on Aging (US)
Publication Details
- Journal
- Fluids and Barriers of the CNS
- Published
- 2025-12-27
- DOI
- https://doi.org/10.1186/s12987-025-00750-w
- Primary Topic
- Gastrointestinal motility and disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00