Deep learning-based classification of pleural malignancy using medical thoracoscopic images
This study presents the first deep learning-based classification model of pleural malignancy using MT images. The model showed excellent diagnostic performance and has the potential to aid real-time clinical decision-making during thoracoscopy by suggesting malignant targets for biopsy or pleurodesis.
Authors
- Jong‐Yeup Kim (ORCID: https://orcid.org/0000-0003-1230-9307)
- Joon Young Choi (ORCID: https://orcid.org/0000-0001-6298-2204)
- S. Ha (ORCID: https://orcid.org/0000-0003-2538-1551)
- Yu Jin Hong (ORCID: https://orcid.org/0000-0002-3042-8337)
- Seong Hyeon Park (ORCID: https://orcid.org/0000-0002-0097-2745)
- Sang Haak Lee (ORCID: https://orcid.org/0000-0001-6259-7656)
- Hyung Woo Kim (ORCID: https://orcid.org/0000-0002-6305-452X)
- Bora Lee (ORCID: https://orcid.org/0000-0002-6322-5712)
- Chang Dong Yeo (ORCID: https://orcid.org/0000-0002-4103-7921)
- Jick Hwan Ha
Institutions
- The Catholic University of Korea Incheon St. Mary's Hospital (KR)
- Konyang University Hospital (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- Translational Lung Cancer Research
- Published
- 2025-10-01
- DOI
- https://doi.org/10.21037/tlcr-2025-588
- Citations
- 1
- Primary Topic
- Pleural and Pulmonary Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.52