Deep Learning-Based Uroflowmetry Curve Analysis Improves the Noninvasive Diagnosis of Lower Urinary Tract Symptoms
AI-based analysis of UFM curve images, enhanced through customized preprocessing, improved diagnostic accuracy in patients with lower urinary tract symptoms, effectively identifying BOO and DUA. This noninvasive method may serve as an adjunct or screening tool to reduce reliance on invasive UDS.
Authors
- Chung Un Lee (ORCID: https://orcid.org/0000-0002-3030-0405)
- Myung Jin Chung (ORCID: https://orcid.org/0000-0002-6271-3343)
- Kwang Jin Ko (ORCID: https://orcid.org/0000-0003-1085-1266)
- Deok Hyun Han (ORCID: https://orcid.org/0000-0002-9962-802X)
- Yungon Lee (ORCID: https://orcid.org/0000-0001-7793-6104)
- J.H. Kim (ORCID: https://orcid.org/0009-0001-9593-701X)
- Jong Hoon Lee (ORCID: https://orcid.org/0009-0004-1292-9957)
Institutions
- Samsung Medical Center (KR)
- Chung-Ang University Hospital (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- International Neurourology Journal
- Published
- 2025-11-26
- DOI
- https://doi.org/10.5213/inj.2550266.133
- Citations
- 1
- Primary Topic
- Urinary Bladder and Prostate Research
- Type
- article
- Field-Weighted Citation Impact
- 1.84
Funders
- National Research Foundation
- National IT Industry Promotion Agency
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Samsung