Agrimi E, Diko A, Carlotti D, Ciardiello A, Borthakur M, Giagu S, Melchionna S, Voena C
"Sapienza" Università di Roma, Dipartimento di Fisica, Piazzale Aldo Moro 2, 00185 Rome, Italy.
Istituto Nazionale di Fisica Nucleare, sezione di Roma, Piazzale Aldo Moro 2, 00185 Rome, Italy.
Eur Phys J Plus. 2023;138(2):182. doi: 10.1140/epjp/s13360-023-03744-5. Epub 2023 Feb 27.
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.
新冠病毒疾病在许多患者中引发肺炎，在最严重的情况下会发展为急性呼吸窘迫综合征（ARDS），需要辅助通气和重症监护。在此背景下，识别有发展为ARDS高风险的患者是早期临床管理、改善临床结局以及优化重症监护病房有限资源使用的关键。我们提出一种基于人工智能的预后系统，该系统通过将肺部计算机断层扫描（CT）、生物力学模拟获得的肺内气流以及动脉血气（ABG）分析作为输入，来预测与动脉血的氧交换。我们在一个已确诊的新冠病例小型临床数据库上开发并研究了该系统的可行性，该数据库中每个患者都有初始CT和各种ABG报告。我们研究了ABG参数的时间演变，并发现其与从CT扫描提取的形态学信息及疾病结局相关。文中展示了预后算法初步版本的 promising 结果。预测患者呼吸效率演变的能力对疾病管理至关重要。
