BACKGROUND AND OBJECTIVE: Severe courses of COVID-19 disease can lead to long-term complications. The post-acute phase of COVID-19 refers to the persistent or new symptoms. This problem is becoming more relevant with the increasing number of patients who have contracted COVID-19 and the emergence of new virus variants. In this case, preventive treatment with corticosteroids can be applied. However, not everyone benefits from the treatment, moreover, it can have severe side effects. Currently, no study would analyze who benefits from the treatment. METHODS: This work introduces a novel approach to the recommendation of Corticosteroid (CS) treatment for patients in the post-acute phase. We have used a novel combination of clinical data, including blood tests, spirometry, and X-ray images from 273 patients. These are very challenging to collect, especially from patients in the post-acute phase of COVID-19. To our knowledge, no similar dataset exists in the literature. Moreover, we have proposed a unique methodology that combines machine learning and deep learning models based on Vision Transformer (ViT) and InceptionNet, preprocessing techniques, and pretraining strategies to deal with the specific characteristics of our data. RESULTS: The experiments have proved that combining clinical data with CXR images achieves 8% higher accuracy than independent analysis of CXR images. The proposed method reached 80.0% accuracy (78.7% balanced accuracy) and a ROC-AUC of 0.89. CONCLUSIONS: The introduced system for CS treatment prediction using our neural network and learning algorithm is unique in this field of research. Here, we have shown the efficiency of using mixed data and proved it on real-world data. The paper also introduces the factors that could be used to predict long-term complications. Additionally, this system was deployed to the hospital environment as a recommendation tool, which admits the clinical application of the proposed methodology.

• Klíčová slova
• Chest X-ray images, Clinical data, Image classification, Post-acute COVID-19, Treatment prediction, Vision transformer,
• Publikační typ
• časopisecké články MeSH

The COVID-19 pandemic has posed an unprecedented threat to the global public health system, primarily infecting the airway epithelial cells in the respiratory tract. Chest X-ray (CXR) is widely available, faster, and less expensive therefore it is preferred to monitor the lungs for COVID-19 diagnosis over other techniques such as molecular test, antigen test, antibody test, and chest computed tomography (CT). As the pandemic continues to reveal the limitations of our current ecosystems, researchers are coming together to share their knowledge and experience in order to develop new systems to tackle it. In this work, an end-to-end IoT infrastructure is designed and built to diagnose patients remotely in the case of a pandemic, limiting COVID-19 dissemination while also improving measurement science. The proposed framework comprises six steps. In the last step, a model is designed to interpret CXR images and intelligently measure the severity of COVID-19 lung infections using a novel deep neural network (DNN). The proposed DNN employs multi-scale sampling filters to extract reliable and noise-invariant features from a variety of image patches. Experiments are conducted on five publicly available databases, including COVIDx, COVID-19 Radiography, COVID-XRay-5K, COVID-19-CXR, and COVIDchestxray, with classification accuracies of 96.01%, 99.62%, 99.22%, 98.83%, and 100%, and testing times of 0.541, 0.692, 1.28, 0.461, and 0.202 s, respectively. The obtained results show that the proposed model surpasses fourteen baseline techniques. As a result, the newly developed model could be utilized to evaluate treatment efficacy, particularly in remote locations.

• Klíčová slova
• COVID-19, Chest X-ray, Deep neural network, Internet of things,
• Publikační typ
• časopisecké články MeSH