Detection and position evaluation of chest percutaneous drainage catheter on chest radiographs using deep learning
The artificial intelligence model for the automatic detection and evaluation of catheter position during chest PCD on chest radiographs demonstrated acceptable diagnostic performance and could assist radiologists and clinicians in the early detection of catheter malposition and malfunction during chest percutaneous catheter drainage.
Authors
- Duk Ju Kim (ORCID: https://orcid.org/0000-0003-1773-4230)
- In Chul Nam (ORCID: https://orcid.org/0000-0001-8406-6645)
- Jeong Sub Lee (ORCID: https://orcid.org/0000-0002-4120-8655)
- Hyeonwoo Kim (ORCID: https://orcid.org/0000-0002-1908-0907)
- Doo Ri Kim (ORCID: https://orcid.org/0000-0001-5263-7693)
- Sung Eun Park (ORCID: https://orcid.org/0000-0001-6093-6888)
- I S Hwang (ORCID: https://orcid.org/0009-0003-7314-0741)
- Jeong Jae Kim (ORCID: https://orcid.org/0000-0001-6952-314X)
Institutions
- Changwon National University (KR)
- Gyeongsang National University (KR)
- Jeju National University (KR)
Publication Details
- Journal
- PLoS ONE
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1371/journal.pone.0305859
- Citations
- 1
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Daegu Gyeongbuk Institute of Science and Technology