Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients: An Artificial Intelligence-Based Approach

Lung Wen Tsai, Kuo Ching Yuan, Sen Kuang Hou, Wei Lin Wu, Chen Hao Hsu, Tyng Luh Liu, Kuang Min Lee, Chiao Hsuan Li, Hann Chyun Chen, Ethan Tu, Rajni Dubey, Chun Fu Yeh, Ray Jade Chen

研究成果: 雜誌貢獻文章同行評審

摘要

Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients’ morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30–70 mm, (ii) 30–60 mm, (iii) 20–60 mm, and (iv) 20–55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20–55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians’ consensus.

原文英語
文章編號490
期刊Biology
11
發行號4
DOIs
出版狀態已發佈 - 4月 2022

ASJC Scopus subject areas

  • 生物化學、遺傳與分子生物學 (全部)
  • 免疫學與微生物學 (全部)
  • 農業與生物科學 (全部)

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