Deep-Learning-Based Multi-Class Classification for Neonatal Respiratory Diseases on Chest Radiographs in Neonatal Intensive Care Units
We introduced a deep-learning-based automated algorithm to classify neonatal respiratory diseases using a large dataset of high-quality, multi-class labeled chest X-ray images, incorporating non-imaging data, which could support neonatologists in making timely and accurate decisions for critically ill newborns.
Authors
- Kyu Hee Park (ORCID: https://orcid.org/0000-0001-8640-3211)
- Ju Sun Heo (ORCID: https://orcid.org/0000-0001-8928-289X)
- Byung Min Choi (ORCID: https://orcid.org/0000-0003-0931-2353)
- I. Yu. Deulin (ORCID: https://orcid.org/0000-0001-9470-4153)
- Huifeng Yun (ORCID: https://orcid.org/0000-0002-0158-1797)
- Sumin Jung (ORCID: https://orcid.org/0009-0003-2556-8399)
- Jae‐Young Kim (ORCID: https://orcid.org/0000-0002-9423-438X)
- Jin Wha Choi
- Hye Won Cho (ORCID: https://orcid.org/0009-0005-9165-7909)
- Jin A Son
Institutions
- Seoul National University (KR)
- Korea University (KR)
- Ansan University (KR)
- Bristol-Myers Squibb (South Korea) (KR)
- Hansol Chemical (South Korea) (KR)
Publication Details
- Journal
- Neonatology
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1159/000545107
- Citations
- 3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Korea University