Corticosteroid treatment prediction using chest X-ray and clinical data
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...
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Elsevier
2024-12-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023004713 |
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| author | Anzhelika Mezina Samuel Genzor Radim Burget Vojtech Myska Jan Mizera Aleksandr Ometov |
| author_facet | Anzhelika Mezina Samuel Genzor Radim Burget Vojtech Myska Jan Mizera Aleksandr Ometov |
| author_sort | Anzhelika Mezina |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3853ebc88caf4caeb1add7cecaedadb4 |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-3853ebc88caf4caeb1add7cecaedadb42025-08-20T02:52:08ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-0124536510.1016/j.csbj.2023.11.057Corticosteroid treatment prediction using chest X-ray and clinical dataAnzhelika Mezina0Samuel Genzor1Radim Burget2Vojtech Myska3Jan Mizera4Aleksandr Ometov5Brno University of Technology, FEEC, Dept. of Telecommunications, Technicka 12, Brno, 616 00, Czech Republic; Corresponding authors.Center for Digital Health, Palacky Univesity Olomouc, Faculty of Medicine and Dentistry, Hnevotinska 976/3, Olomouc 779 00, Czech Republic; Corresponding authors.Brno University of Technology, FEEC, Dept. of Telecommunications, Technicka 12, Brno, 616 00, Czech RepublicBrno University of Technology, FEEC, Dept. of Telecommunications, Technicka 12, Brno, 616 00, Czech RepublicCenter for Digital Health, Palacky Univesity Olomouc, Faculty of Medicine and Dentistry, Hnevotinska 976/3, Olomouc 779 00, Czech RepublicElectrical Engineering Unit, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, 33720, FinlandBackground 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.http://www.sciencedirect.com/science/article/pii/S2001037023004713Image classificationChest X-ray imagesVision transformerTreatment predictionClinical dataPost-acute COVID-19 |
| spellingShingle | Anzhelika Mezina Samuel Genzor Radim Burget Vojtech Myska Jan Mizera Aleksandr Ometov Corticosteroid treatment prediction using chest X-ray and clinical data Computational and Structural Biotechnology Journal Image classification Chest X-ray images Vision transformer Treatment prediction Clinical data Post-acute COVID-19 |
| title | Corticosteroid treatment prediction using chest X-ray and clinical data |
| title_full | Corticosteroid treatment prediction using chest X-ray and clinical data |
| title_fullStr | Corticosteroid treatment prediction using chest X-ray and clinical data |
| title_full_unstemmed | Corticosteroid treatment prediction using chest X-ray and clinical data |
| title_short | Corticosteroid treatment prediction using chest X-ray and clinical data |
| title_sort | corticosteroid treatment prediction using chest x ray and clinical data |
| topic | Image classification Chest X-ray images Vision transformer Treatment prediction Clinical data Post-acute COVID-19 |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023004713 |
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