Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives

Rehman, Amir; Xing, Huanlai; Khan, Muhammad Adnan; Hussain, Mehboob; Hussain, Abid; Gulzar, Nighat

doi:10.1016/j.bspc.2023.104642

Cited by 9 publications

(3 citation statements)

References 111 publications

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“…Studies have proposed several machine learning models that can accurately predict COVID-19 disease severity [ 10 – 12 ]. A comprehensive bibliometric analysis was performed to summarize all accessible techniques for detecting, classifying, monitoring and locating COVID-19 patients, including AI, big data and smart applications [ 13 ]. They concluded that AI-assisted CT was better at diagnosing COVID-19 pneumonia due to its high precision and low false-negative rates.…”

Section: Introductionmentioning

confidence: 99%

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Yin,

Xu,

Zhu

et al. 2024

BMC Med Imaging

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Background Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Methods Asymptomatic carriers were from Yangzhou Third People’s Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Conclusions Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

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Section: Introductionmentioning

confidence: 99%

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Yin,

Xu,

Zhu

et al. 2024

BMC Med Imaging

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“…Artificial Intelligence (AI) built initially for TB diagnosis, was diverted to COVID-19 [1]. This medical device is Indonesia's first innovation to detect COVID-19 through breathing, and the application is connected to a cloud computing system to get real-time diagnosis results [2]. This tool requires large quantities of quality gaskets during its operation, as shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Quantity and Quality of GeNose 19 Medical Device Gaskets Using Piercing Tools

Khoryanton,

Nugroho,

Fitrianti Sugiono

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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The Ge Nose C19 medical device gasket is a product from a manufacturing company in Yogya. Gaskets must be of good quality to ensure vacuum or prevent air leakage from entering the test chamber. The core problem in the production process is the quality problem of many rejects and the quantity not yet meeting the production target. The observation results show that the gasket reject problem is found in the four holes that are formed, which do not match the dimensions or are torn. Making four holes in the gasket made from neoprene rubber is done through a drilling process, which can risk tearing or damage. The type of drill bit used can affect the success of drilling gasket holes. The process of making four holes in the gasket using the piercing method is thought to be able to increase the quality and quantity of gasket products. This research aims to increase the quantity and quality of Ge Nose 19 medical device gaskets using a piercing tool. Research methods include problem identification, literature study, design, manufacture, and testing of piercing tools. The test parameters are time and product quality, namely the shape and position of the hole. The results of this research show a reduction in the cycle time of the hole-making process by 53.7%, so production capacity has increased by 51.8%. The quality of gasket production has increased; of the 40 samples tested, 0 gaskets were rejected.

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“…If used effectively and in accordance with regulations, it allows us to access care (including rehabilitation), monitoring and remote therapeutic treatment, without the need for physical meetings. Telemedicine has recently been applied in smart engineering [ 20 , 21 , 22 ] following the boost provided by the COVID-19 pandemic in this area, in addition to other areas [ 20 ].…”

mentioning

confidence: 99%

Synergizing Intelligence and Building a Smarter Future: Artificial Intelligence Meets Bioengineering

Giansanti

2023

Bioengineering

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Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives

Cited by 9 publications

References 111 publications

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Increasing the Quantity and Quality of GeNose 19 Medical Device Gaskets Using Piercing Tools

Synergizing Intelligence and Building a Smarter Future: Artificial Intelligence Meets Bioengineering

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