Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray

Endotracheal tubes (ETTs) provide a vital connection between the ventilator and patient; however, 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 expertise 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 DENSENET169) 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.

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Transfer Learning Method Evaluation for Automatic Pediatric Chest X-Ray Image Segmentation

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) ◽

10.1109/iwssip48289.2020.9145401 ◽

2020 ◽

Author(s):

Gabriel Bras ◽

Vandecia Fernandes ◽

Anselmo Cardoso de Paiva ◽

Geraldo Braz Junior ◽

Luis Rivero

Keyword(s):

Image Segmentation ◽

Transfer Learning ◽

Learning Method ◽

Method Evaluation ◽

X Ray ◽

Pediatric Chest ◽

Chest X Ray

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A Transfer Learning Method for Pneumonia Classification and Visualization

Applied Sciences ◽

10.3390/app10082908 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2908 ◽

Cited By ~ 5

Author(s):

Juan Luján-García ◽

Cornelio Yáñez-Márquez ◽

Yenny Villuendas-Rey ◽

Oscar Camacho-Nieto

Keyword(s):

Transfer Learning ◽

Causes Of Death ◽

State Of The Art ◽

Machine Learning Algorithms ◽

Learning Method ◽

X Ray ◽

Chest X Ray ◽

Aided Diagnosis ◽

Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

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Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

International Journal of Biomedical Imaging ◽

10.1155/2020/8889023 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Arun Sharma ◽

Sheeba Rani ◽

Dinesh Gupta

Keyword(s):

Artificial Intelligence ◽

Infectious Diseases ◽

Transfer Learning ◽

Rapid Screening ◽

Learning Approach ◽

Classification Models ◽

Classification Problems ◽

X Ray ◽

Healthcare Crisis ◽

Chest X Ray

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

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