An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis

Abstract

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 $$97.72\%\pm 1.57$$ 97.72 % ± 1.57 , a sensitivity of $$97.76\%\pm 4.08$$ 97.76 % ± 4.08 , and a specificity of $$98.87\%\pm 2.09$$ 98.87 % ± 2.09 , indicating a high level of prediction accuracy.