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COVID-19 therapy optimization by AI-driven biomechanical simulations
E Agrimi1,2,3, A Diko4, D Carlotti1,2
1"Sapienza" Università di Roma, Dipartimento di Fisica, Piazzale Aldo Moro 2, 00185 Rome, Italy.
Insights
This study introduces an AI system to predict Acute Distress Respiratory Syndrome (ARDS) in COVID-19 patients. By analyzing lung CT scans and blood gas data, the system aims to improve early clinical management and patient outcomes.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pulmonology
Background:
- COVID-19 can lead to severe pneumonia and Acute Distress Respiratory Syndrome (ARDS).
- Early identification of ARDS risk is crucial for effective patient management and resource allocation in intensive care units.
Purpose of the Study:
- To develop and assess an AI-based prognostic system for predicting respiratory efficiency in COVID-19 patients.
- To identify patients at high risk of developing ARDS for timely clinical intervention.
Main Methods:
- Utilized lung Computed Tomography (CT) scans, biomechanical simulations of lung air flux, and Arterial Blood Gas (ABG) analysis as input.
- Developed a preliminary AI algorithm to predict oxygen exchange and correlate ABG parameters with CT-derived morphological information and disease outcomes.
- Investigated the system's feasibility on a small clinical database of COVID-19 patients.
Main Results:
- Demonstrated promising results from a preliminary version of the AI prognostic algorithm.
- Found correlations between the time evolution of ABG parameters, CT scan morphological data, and patient outcomes.
- Showcased the potential for predicting the progression of respiratory efficiency in COVID-19 patients.
Conclusions:
- The proposed AI system shows potential for predicting ARDS development in COVID-19 patients.
- Integrating CT imaging, biomechanical simulations, and ABG analysis can enhance prognostic accuracy.
- Predicting respiratory efficiency evolution is vital for optimizing COVID-19 patient care and intensive care unit resource management.
Abstract:
The COVID-19 disease causes pneumonia in many patients that in the most serious cases evolves into the Acute Distress Respiratory Syndrome (ARDS), requiring assisted ventilation and intensive care. In this context, identification of patients at high risk of developing ARDS is a key point for early clinical management, better clinical outcome and optimization in using the limited resources available in the intensive care units. We propose an AI-based prognostic system that makes predictions of oxygen exchange with arterial blood by using as input lung Computed Tomography (CT), the air flux in lungs obtained from biomechanical simulations and Arterial Blood Gas (ABG) analysis. We developed and investigated the feasibility of this system on a small clinical database of proven COVID-19 cases where the initial CT and various ABG reports were available for each patient. We studied the time evolution of the ABG parameters and found correlation with the morphological information extracted from CT scans and disease outcome. Promising results of a preliminary version of the prognostic algorithm are presented. The ability to predict the evolution of patients' respiratory efficiency would be of crucial importance for disease management.
