Using transfer learning method to develop an artificial intelligence assisted triaging for endotracheal tube position on chest X-ray

Kuo Ching Yuan, Lung Wen Tsai, Kevin S. Lai, Sing Teck Teng, Yu Sheng Lo, Syu Jyun Peng

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Endotracheal tubes (ETTs) provide a vital connection between the ventilator and patient; how-ever, improper placement can hinder ventilation efficiency or injure the patient. Chest X-ray (CXR) is the most common approach to confirming ETT placement; however, technicians require considerable exper-tise in the interpretation of CXRs, and formal reports are often delayed. In this study, we developed an artificial intelligence-based triage system to enable the automated assessment of ETT placement in CXRs. Three intensivists performed a review of 4293 CXRs obtained from 2568 ICU patients. The CXRs were labeled “CORRECT” or “INCORRECT” in accordance with ETT placement. A region of interest (ROI) was also cropped out, including the bilateral head of the clavicle, the carina, and the tip of the ETT. Transfer learning was used to train four pre-trained models (VGG16, INCEPTION_V3, RESNET, and DENSE-NET169) and two models developed in the current study (VGG16_Tensor Projection Layer and CNN_Tensor Projection Layer) with the aim of differentiating the placement of ETTs. Only VGG16 based on ROI images presented acceptable performance (AUROC = 92%, F1 score = 0.87). The results obtained in this study demonstrate the feasibility of using the transfer learning method in the development of AI models by which to assess the placement of ETTs in CXRs.
原文英語
文章編號1844
期刊Diagnostics
11
發行號10
DOIs
出版狀態已發佈 - 10月 2021

ASJC Scopus subject areas

  • 臨床生物化學

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