An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis
| dc.contributor.author | Farahat, Ibrahim Shawky | |
| dc.contributor.author | Sharafeldeen, Ahmed | |
| dc.contributor.author | Ghazal, Mohammed | |
| dc.contributor.author | Alghamdi, Norah Saleh | |
| dc.date.accessioned | 2024-08-20T05:14:10Z | |
| dc.date.available | 2024-08-20T05:14:10Z | |
| dc.date.issued | 2024-01-08 | |
| dc.description | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus emerged at the end of 2019 and caused the coronavirus disease of 2019 (COVID-19)1. The virus quickly spread across the world, resulting in severe impacts on the global economy and public health2. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic due to its rapid spread3. | |
| dc.description.abstract | The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov–Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of 97.72 % ± 1.57 , a sensitivity of 97.76 % ± 4.08 , and a specificity of 98.87 % ± 2.09 , indicating a high level of prediction accuracy. Keywords :Diagnosis ,Translational research | |
| dc.identifier.citation | Farahat, I. S., Sharafeldeen, A., Ghazal, M., Alghamdi, N. S., Mahmoud, A., Connelly, J., ... & El-Baz, A. (2024). An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis. Scientific Reports, 14(1), 851. | |
| dc.identifier.doi | https://doi.org/10.1038/s41598-023-51053-9 | |
| dc.identifier.uri | https://repository.adu.ac.ae/handle/1/6221 | |
| dc.language.iso | en | |
| dc.publisher | Nature Research | |
| dc.title | An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis | |
| dc.type | Reports |