Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence–Based 3D T1 MRI Volumetric Analysis
We demonstrated a machine learning algorithm capable of diagnosing iNPH from a 3D T1-weighted MRI that is robust to the failure. We propose a method to scan large numbers of 3D T1-weighted MRIs with minimal human intervention, making possible large-scale iNPH screening.
Authors
- Sun Ju Chung (ORCID: https://orcid.org/0000-0003-4118-8233)
- Seungjun Lee
- Kyu Sung Choi (ORCID: https://orcid.org/0000-0002-5175-3307)
- Suyoung Yun
- Jae‐Sung Lim (ORCID: https://orcid.org/0000-0001-6157-2908)
- Chong Hyun Suh (ORCID: https://orcid.org/0000-0002-4737-0530)
- Woo Hyun Shim (ORCID: https://orcid.org/0000-0002-7251-2916)
- Sungyang Jo (ORCID: https://orcid.org/0000-0001-5097-2340)
- Joonhyung Lee (ORCID: https://orcid.org/0000-0002-6924-6012)
- Hwon Heo (ORCID: https://orcid.org/0000-0002-6103-4680)
- Jinyoung Kim (ORCID: https://orcid.org/0000-0002-3810-8549)
- Jae‐Hong Lee (ORCID: https://orcid.org/0000-0001-7368-4560)
- Ho Sung Kim
- Sang Joon Kim (ORCID: https://orcid.org/0000-0001-7070-7333)
- Dana Kim (ORCID: https://orcid.org/0000-0001-7376-4915)
- Wooseok Jung (ORCID: https://orcid.org/0000-0003-4765-1233)
Institutions
- Ulsan College (KR)
- Asan Medical Center (KR)
- Seoul National University Hospital (KR)
- LG (United States) (US)
- University of Ulsan (KR)
- Research Institute of Radiology (BY)
- Inje University Busan Paik Hospital (KR)
- Naver (South Korea) (KR)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2024-09-09
- DOI
- https://doi.org/10.3174/ajnr.a8489
- Citations
- 8
- Primary Topic
- Cerebrospinal fluid and hydrocephalus
- Type
- article
- Field-Weighted Citation Impact
- 2.15
Funders
- National Research Foundation
- Korea Health Industry Development Institute
- National Research Foundation of Korea