Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period

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dc.creatorLi, Yifan (TTU)
dc.creatorPang, Alan W (TTUHSC)
dc.creatorZeitouni, Ferris (TTUHSC)
dc.creatorMateja, Kirby (TTUHSC)
dc.creatorGriswold, John A (TTUHSC)
dc.creatorChong, Jo Woon (TTU)
dc.date.accessioned2023-03-14T16:34:29Z
dc.date.available2023-03-14T16:34:29Z
dc.date.issued2022
dc.description© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en_US
dc.description.abstractThe abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians’ experience and expertise. Additionally, no correlation has been shown between these patients’ inhalation injury grades and outcomes. In this paper, we propose a novel inhalation injury grading method which uses deep learning algorithms in bronchoscopy images to determine the injury grade from the carbonaceous deposits, blistering, and fibrin casts in the bronchoscopy images. The proposed method adopts transfer learning and data augmentation concepts to enhance the accuracy performance to avoid overfitting. We tested our proposed model on the bronchoscopy images acquired from eighteen patients who had suffered inhalation injuries, with the degree of severity 1, 2, 3, 4, 5, or 6. As performance metrics, we consider accuracy, sensitivity, specificity, F-1 score, and precision. Experimental results show that our proposed method, with both transfer learning and data augmentation components, provides an overall 86.11% accuracy. Moreover, the experimental results also show that the performance of the proposed method outperforms the method without transfer learning or data augmentation.en_US
dc.identifier.citationLi Y, Pang AW, Zeitouni J, Zeitouni F, Mateja K, Griswold JA, Chong JW. Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period. Sensors. 2022; 22(23):9430. https://doi.org/10.3390/s22239430en_US
dc.identifier.urihttps://doi.org/10.3390/s22239430
dc.identifier.urihttps://hdl.handle.net/2346/91812
dc.language.isoengen_US
dc.subjectinhalation injuryen_US
dc.subjectdeep learningen_US
dc.subjectconvolution neural networks (cnn)en_US
dc.subjecttransfer learningen_US
dc.titleInhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Perioden_US
dc.typeArticleen_US

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