Clinical relevance of the routine daily chest X-Ray in the surgical intensive care unit

Resnick, Shelby; Inaba, Kenji; Karamanos, Efstathios; Skiada, Dimitra; Dollahite, James A.; Okoye, Obi; Talving, Peep; Δημητριάδης, Δημήτριος

doi:10.1016/j.amjsurg.2016.09.059

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…Extensive data demonstrates a low yield of clinical information when routine daily chest radiography is done for diverse ICU patient populations, including in postoperative surgical and cardiothoracic patients [1–7]. Studies have suggested that use of “on demand” chest radiography in the surgical ICU setting may increase the rate of positive findings found compared to “routine” chest radiography methodology [8,9]. Additionally, there are controlled clinical trials that demonstrate that moving from “routine” to “on-demand” chest radiography has no detrimental impact on patient outcome, though these trials have excluded cardiothoracic patients [10,11].…”

Section: Introductionmentioning

confidence: 99%

Single intervention for a reduction in portable chest radiography (pCXR) in cardiovascular and surgical/trauma ICUs and associated outcomes

Tonna

Kawamoto

Presson

et al. 2018

Journal of Critical Care

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Section: Introductionmentioning

confidence: 99%

Single intervention for a reduction in portable chest radiography (pCXR) in cardiovascular and surgical/trauma ICUs and associated outcomes

Tonna

Kawamoto

Presson

et al. 2018

Journal of Critical Care

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“…Chest radiography (chest X‐rays) is one of the most cost‐effective and widely used clinical diagnostic procedures [4], with health professionals using it in their professions to identify and diagnose deadly illnesses such as tuberculosis, lung nodules, pneumonia, and the early stages of cancer [5]. Chest X‐rays provide an abundance of details about a person's health history.…”

Section: Introductionmentioning

confidence: 99%

Optimizing chest tuberculosis image classification with oversampling and transfer learning

Alqahtani,

Abu Al‐Haija,

Alsulami

et al. 2023

IET Image Processing

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Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score.

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“…They choose the x-ray imaging analysis for the identification of coronavirus [ 44 ]. Today, one of the most economical and conventional clinical diagnostic methods is chest radiography (chest x-rays) [ 78 ]. Even in impoverished countries, modern digital radiography machines are easily affordable [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

Malik

Anees

din

et al. 2022

Multimed Tools Appl

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Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19’s medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar’s, and ANOVA tests also showed the robustness of the proposed model.

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Clinical relevance of the routine daily chest X-Ray in the surgical intensive care unit

Cited by 12 publications

References 34 publications

Single intervention for a reduction in portable chest radiography (pCXR) in cardiovascular and surgical/trauma ICUs and associated outcomes

Single intervention for a reduction in portable chest radiography (pCXR) in cardiovascular and surgical/trauma ICUs and associated outcomes

Optimizing chest tuberculosis image classification with oversampling and transfer learning

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

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