Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes

Ortiz, Anthony; Trivedi, Anusua; Desbiens, Jocelyn; Blazes, Marian; Robinson, Caleb; Gupta, Sunil; Dodhia, Rahul; Bhatraju, Pavan K.; Liles, W. Conrad; Lee, Aaron; Ferres, Juan M. Lavista

doi:10.1038/s41598-022-05532-0

Cited by 32 publications

(20 citation statements)

References 24 publications

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“…Finally, Ortiz et al. [12] proposed a novel patient‐level algorithm which had 2D representation by aggregating the chest CT volume. Then, a multitask model was presented for finding four pulmonary lesions specific to Covid‐19 infection.…”

Section: Related Workmentioning

confidence: 99%

Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease

Kordnoori

Sabeti

Mostafaei

et al. 2023

IET Image Processing

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Covid‐19 caused by the SARS‐CoV2 virus has become a pandemic all over the world. By growing in a number of cases, there is a need for clinical decision‐making system based on machine learning models. Most of the previous studies have examined only one task, while the detection and identification of infectious area are conducted simultaneously in the real world. Thus, the present study aims to propose a multi‐task model which can perform automatic classification‐segmentation for screening Covid‐19 pneumonia by using chest CT imaging. This model includes a common encoder for feature representation, one decoder for segmentation, and a multi‐layer perceptron for classification, respectively. The proposed model can evaluate three datasets, along with the effect of images size on the output of the model. The outputs were examined in both multi‐task and single‐task learning. The result indicates that the effect of multi‐task is significant in improving the results, which can increase the outputs of each task performance to 95.40% accuracy in classification and 95.40% in segmentation. Further, the model represented the highest results among the state‐of‐the‐art methods. The proposed model can be applied as a primary screening tool to help primary service staff in better referral of the suspected patients to specialists.

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Section: Related Workmentioning

confidence: 99%

Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease

Kordnoori

Sabeti

Mostafaei

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Also, the F1 Score is an overall performance metric made up of precision and recall. The Harmonic mean of precision and recall represents it [133].…”

Section: Comparison Metricsmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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“…Three different models were explored for assessing the prognosis where model A included clinical variables, model B treated their CT severity score as an independent variable and model C included the lesion quantification from their segmentation model. The C-statistic outcome was 0.82 for model A, 0.89 for model B, and 0.90 for model C. Authors in [ 151 ] developed a deep learning model that combines clinical variables with CT segmentation for diagnosis and prognosis of COVID-19 patients. The dataset that was examined included 839 COVID-19 positive patients, 874 patients with viral pneumonia, and 758 normal patients.…”

Section: Covid-19 Prognostic and Longitudinal Modelsmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes

Cited by 32 publications

References 24 publications

Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease

Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease

Machine learning applications for COVID-19 outbreak management

A Comprehensive Review of Machine Learning Used to Combat COVID-19

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