ArticleScientific reports2024
An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.Scientific reports · 2025Article
- IoT-based bed and ventilator management system during the COVID-19 pandemic.Scientific reports · 2025Article
- Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images.Scientific reports · 2025Article
- A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.Scientific reports · 2025Article
- AI based medical imagery diagnosis for COVID-19 disease examination and remedy.Scientific reports · 2025Article
- A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness.European journal of radiology open · 2024Article
- The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model.BMC pulmonary medicine · 2024Article
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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 [Formula: see text], a sensitivity of [Formula: see text], and a specificity of [Formula: see text], indicating a high level of prediction accuracy.
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