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Tsai LW, Yuan KC, Hou SK, Wu WL, Hsu CH, Liu TL, Lee KM, Li CH, Chen HC, Tu E, Dubey R, Yeh CF, Chen RJ. Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients: An Artificial Intelligence-Based Approach. BIOLOGY 2022; 11:490. [PMID: 35453690 PMCID: PMC9027916 DOI: 10.3390/biology11040490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/24/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Lung-Wen Tsai
- Department of Medicine Research, Taipei Medical University Hospital, Taipei 11031, Taiwan;
- Department of Information Technology Office, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 11031, Taiwan
| | - Kuo-Ching Yuan
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Department of Surgery, Da Chien General Hospital, Miaoli 36052, Taiwan
| | - Sen-Kuang Hou
- Department of Emergency Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan;
- Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Wei-Lin Wu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Chen-Hao Hsu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Tyng-Luh Liu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Kuang-Min Lee
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Chiao-Hsuan Li
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Hann-Chyun Chen
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Ethan Tu
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Chun-Fu Yeh
- Taiwan AI Labs, Taipei 10351, Taiwan; (W.-L.W.); (C.-H.H.); (T.-L.L.); (K.-M.L.); (C.-H.L.); (H.-C.C.); (E.T.)
| | - Ray-Jade Chen
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Division of Infection Diseases, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
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