CT scans can also provide detailed structural information, such as the extent of lung involvement and quantitative analysis of NCP lesions associated with prognostic value in patients with COVID-19. CT imaging exhibits the advantage of faster processing time as compared with the molecular diagnostic test. As an alternative, computed tomography (CT) can be utilized for the initial screening of NCP. Although this approach is considered the most effective, it is both time-consuming and has a high rate of false negatives. Therefore, it is critical to identify high-risk patients among those with advanced COVID-19 to deliver early intensive care.ĬOVID-19 is diagnosed using viral nucleic acid detection employed by reverse transcription–polymerase chain reaction (RT-PCR). A recent study reported that more than 60% of patients who progressed to a severe stage of NCP died. Some patients with COVID-19 progressed to novel coronavirus pneumonia (NCP), which can lead to severe acute respiratory failure, multiple organ failure, and, in some cases, death. Since then, the COVID-19 pandemic has rapidly propagated across the world via airborne person-to-person transmission. In December 2019, SARS-CoV-2, also called COVID-19, was first detected in Wuhan, China. The developed model can be used as a predictive tool for interventions in aggressive therapies. Results: Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups.Ĭonclusions: Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features.
A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Methods: We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. Objective: The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Identification of high-risk cases is critical for early intervention. Asian/Pacific Island Nursing Journal 10 articlesĮmail: Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities.JMIR Bioinformatics and Biotechnology 32 articles.JMIR Biomedical Engineering 68 articles.Journal of Participatory Medicine 78 articles.JMIR Perioperative Medicine 89 articles.JMIR Rehabilitation and Assistive Technologies 201 articles.JMIR Pediatrics and Parenting 279 articles.Interactive Journal of Medical Research 306 articles.JMIR Public Health and Surveillance 1141 articles.Journal of Medical Internet Research 7471 articles.
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