Deep learning‐based classification of acute scrotum using single ultrasound images
A deep learning model demonstrated promising diagnostic performance in differentiating acute scrotal emergencies using single US images. Its feasibility was preliminarily assessed in a small pilot study. These findings support further investigation, with larger and more balanced multicentre studies required to establish clinical utility and effective workflow integration.
Authors
- Won Ki Lee (ORCID: https://orcid.org/0000-0001-7172-0636)
- Sung Gon Park (ORCID: https://orcid.org/0000-0002-7677-2854)
- Cheol Young Oh (ORCID: https://orcid.org/0000-0001-8794-1855)
- Young Goo Lee (ORCID: https://orcid.org/0000-0003-0339-7763)
- Seong Ho Lee (ORCID: https://orcid.org/0000-0003-3142-2233)
- Sahyun Pak (ORCID: https://orcid.org/0000-0001-7015-4943)
- Ji Young Woo (ORCID: https://orcid.org/0000-0002-6200-0159)
- Sung Tae Cho (ORCID: https://orcid.org/0000-0002-4691-6159)
- Jong Keun Kim (ORCID: https://orcid.org/0000-0001-6507-2628)
Institutions
- Hallym University (KR)
- Sacred Heart Hospital (NG)
- Sacred Heart Hospital (US)
- Hallym University Kangnam Sacred Heart Hospital (KR)
- Sacred Heart Hospital (US)
- Hallym University Dongtan Sacred Heart Hospital (KR)
- Hallym University Sacred Heart Hospital (KR)
- Hallym University Chuncheon Sacred Heart Hospital (KR)
Publication Details
- Journal
- British Journal of Urology
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1111/bju.70091
- Citations
- 1
- Primary Topic
- Genital Health and Disease
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- Hallym University
- Hallym University Medical Center