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  1. Article ; Online: Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients

    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

    Biology, Vol 11, Iss 490, p

    An Artificial Intelligence-Based Approach

    2022  Volume 490

    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 ... ...

    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.
    Keywords endotracheal intubation ; endotracheal tube ; chest X-ray ; carina ; clavicle ; Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy

    Ching-Yao Tsai / Chueh-Tan Chen / Guan-An Chen / Chun-Fu Yeh / Chin-Tzu Kuo / Ya-Chuan Hsiao / Hsiao-Yun Hu / I-Lun Tsai / Ching-Hui Wang / Jian-Ren Chen / Su-Chen Huang / Tzu-Chieh Lu / Lin-Chung Woung

    International Journal of Environmental Research and Public Health, Vol 19, Iss 1204, p

    2022  Volume 1204

    Abstract: Deep learning (DL) algorithms are used to diagnose diabetic retinopathy (DR). However, most of these algorithms have been trained using global data or data from patients of a single region. Using different model architectures (e.g., Inception-v3, ... ...

    Abstract Deep learning (DL) algorithms are used to diagnose diabetic retinopathy (DR). However, most of these algorithms have been trained using global data or data from patients of a single region. Using different model architectures (e.g., Inception-v3, ResNet101, and DenseNet121), we assessed the necessity of modifying the algorithms for universal society screening. We used the open-source dataset from the Kaggle Diabetic Retinopathy Detection competition to develop a model for the detection of DR severity. We used a local dataset from Taipei City Hospital to verify the necessity of model localization and validated the three aforementioned models with local datasets. The experimental results revealed that Inception-v3 outperformed ResNet101 and DenseNet121 with a foreign global dataset, whereas DenseNet121 outperformed Inception-v3 and ResNet101 with the local dataset. The quadratic weighted kappa score ( <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math> ) was used to evaluate the model performance. All models had 5–8% higher κ for the local dataset than for the foreign dataset. Confusion matrix analysis revealed that, compared with the local ophthalmologists’ diagnoses, the severity predicted by the three models was overestimated. Thus, DL algorithms using artificial intelligence based on global data must be locally modified to ensure the applicability of a well-trained model to make diagnoses in clinical environments.
    Keywords diabetic retinopathy ; deep learning algorithms ; model localised ; Taiwan ; predict ; Medicine ; R
    Subject code 006
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Molecular Characteristics of Disease-Causing and Commensal Staphylococcus lugdunensis Isolates from 2003 to 2013 at a Tertiary Hospital in Taiwan.

    Chun-Fu Yeh / Tsui-Ping Liu / Chun-Wen Cheng / Shih-Cheng Chang / Ming-Hsun Lee / Jang-Jih Lu

    PLoS ONE, Vol 10, Iss 8, p e

    2015  Volume 0134859

    Abstract: Staphylococcus lugdunensis can cause community- and healthcare-associated infections. This study investigated the molecular characteristics of S. lugdunensis isolates collected at our hospital and compared the characteristics of the infectious and ... ...

    Abstract Staphylococcus lugdunensis can cause community- and healthcare-associated infections. This study investigated the molecular characteristics of S. lugdunensis isolates collected at our hospital and compared the characteristics of the infectious and commensal isolates.We collected the S. lugdunensis isolates between 2003 and 2013. The antimicrobial resistance test, SCCmec typing, accessory gene regulator (agr) typing, pulsed-field gel electrophoresis (PFGE), and δ-like hemolysin activity were performed.In total, 118 S. lugdunensis isolates were collected, of which 67 (56.8%) were classified into the infection group and 51 (43.2%) into the commensal group. The oxacillin resistance rate was 36.4%. The most common SCCmec types were SCCmec types V (51.4%) and II (32.6%). In total, 34 pulsotypes were identified. The PFGE typing revealed five clones (pulsotypes A, J, M, N, and P) at our hospital. Pulsotypes A and N caused the spread of high oxacillin resistance. In total, 10.2% (12 of 118) of the isolates lacked δ-like hemolysin activity. Compared with the infection group, the commensal group showed a higher percentage of multiple drug resistance and carried a higher percentage of SCCmec type II (11 of 22, 50% and 3 of 21, 14.3%) and a lower percentage of SCCmec type V (8 of 22, 36.4% and 14 of 21, 66.7%). The commensal group (27 PFGE types) showed higher genetic diversity than did the infection group (20 PFGE types). No difference was observed in the distribution of the five main pulsotypes, agr typing, and the presence of δ-like hemolysin activity between the two groups.Five main clones were identified at our hospital. The commensal group showed higher genetic diversity, had a higher percentage of multidrug resistance, and carried a higher percentage of SCCmec type II and a lower percentage of SCCmec type V than did the infection group.
    Keywords Medicine ; R ; Science ; Q
    Subject code 630
    Language English
    Publishing date 2015-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening

    Chun-Fu Yeh / Hsien-Tzu Cheng / Andy Wei / Keng-Chi Liu / Mong-Chi Ko / Po-Chen Kuo / Ray-Jade Chen / Po-Chang Lee / Jen-Hsiang Chuang / Chi-Mai Chen / Nai-Kuan Chou / Yeun-Chung Chang / Kuan-Hua Chao / Yi-Chin Tu / Tyng-Luh Liu

    Abstract: We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent ... ...

    Abstract We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.
    Keywords covid19
    Publisher arxiv
    Document type Article
    Database COVID19

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