Optimizing Catheter Verification
This study illustrates the potential of AI-based models in accurately and reliably determining CVC placement in chest x-rays. The proposed method shows high accuracy and offers improved interpretability, important for clinical decision-making. The findings also highlight the importance of dataset quality and diversity in training AI models for medical image analysis.
Authors
- Yannic Elser
- Axel Saalbach (ORCID: https://orcid.org/0000-0002-1535-2100)
- Heinrich Schulz
- Malte Maria Sieren (ORCID: https://orcid.org/0000-0002-3669-2892)
- Jörg Barkhausen (ORCID: https://orcid.org/0000-0001-8937-1198)
- Mattias P. Heinrich (ORCID: https://orcid.org/0000-0002-7489-1972)
- Lennart Berkel (ORCID: https://orcid.org/0009-0006-8024-6992)
- Jonas Stroeder (ORCID: https://orcid.org/0000-0001-8498-6134)
- Lasse Hansen
- Malte Michel Multusch
Institutions
- Maine Farmland Trust (US)
- University of Lübeck (DE)
Publication Details
- Journal
- Investigative Radiology
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1097/rli.0000000000001126
- Citations
- 2
- Primary Topic
- Central Venous Catheters and Hemodialysis
- Type
- article
- Field-Weighted Citation Impact
- 1.49