Presentation + Paper
2 April 2024 Classification of endotracheal tube position in chest x-rays images
Author Affiliations +
Abstract
The precise placement of catheter tubes and lines is crucial for providing optimal care to critically ill patients. However, the challenge of mispositioning these tubes persists. The timely detection and correction of such errors are extremely important, especially considering the increased demand for these interventions, as seen during the COVID-19 pandemic. Unfortunately, manual diagnosis is prone to error, particularly under stressful conditions, highlighting the necessity for automated solutions. This research addresses this challenge by utilizing deep learning techniques to automatically detect and classify the positions of endotracheal tubes (ETTs) in chest x-ray images. Our approach builds upon recent advancements in deep learning for medical image analysis, providing a sophisticated solution to a critical healthcare challenge. The proposed model achieves remarkable performance, with the area under the ROC scores ranging from 0.961 to 0.993 and accuracy values ranging from 0.961 to 0.999. These results emphasize the effectiveness of the model in accurately classifying ETT positions, highlighting its potential clinical utility. Through this study, we introduce an innovative application of AI in medical diagnostics, with considerations for advancing healthcare practices.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William J. Sehnert, and Axel Wismüller "Classification of endotracheal tube position in chest x-rays images", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 129300L (2 April 2024); https://doi.org/10.1117/12.3006899
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KEYWORDS
Medical imaging

Biomedical applications

Chest imaging

Artificial intelligence

Education and training

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