Nicolò Marco, Adraman Altin, Risoli Camilla, Menta Anna, Renda Francesco, Tadiello Michele, Palmieri Sara, Lechiara Marco, Colombi Davide, Grazioli Luigi, Natale Matteo Pio, Scardino Matteo, Demeco Andrea, Foresti Ruben, Montanari Attilio, Barbato Luca, Santarelli Mirko, Martini Chiara
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy.
Department of Neuroradiology, University Hospital of Padova, Via Giustiniani 2, 35128 Padova, Italy.
Diagnostics (Basel). 2024 May 8;14(10):985. doi: 10.3390/diagnostics14100985.
(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients' age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland-Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1-R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1-R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients.
(1) 背景：计算机断层扫描（CT）在新型冠状病毒肺炎（COVID-19）的特征描述和随访中起着至关重要的作用。已经实施了几种评分系统来正确评估感染严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的患者所累及的肺实质，如视觉定量评估评分（VQAS）和基于软件的定量评估评分（SBQAS），以帮助管理SARS-CoV-2感染患者。本研究旨在调查和比较VQAS和SBQAS与两种不同类型基于人工智能（AI）的软件在SARS-CoV-2感染患者中的诊断准确性。(2) 方法：这是一项回顾性研究；共纳入90例患者，纳入标准如下：患者年龄超过18岁，COVID-19检测呈阳性，且于2021年3月至6月期间进行了胸部平扫CT扫描。VQAS由独立评估，SBQAS使用两种不同的人工智能驱动软件程序（Icolung和CT-COPD）进行。采用组内相关系数（ICC）统计指标和Bland-Altman图。(3) 结果：放射科医生（R1和R2）对CT图像中累及的肺实质的VQAS的一致性评分良好（ICC = 0.871）。两种软件类型对SBQAS的一致性评分中等（ICC = 0.584）。Icolung与视觉评估中位数（中位数R1-R2）之间的一致性良好（ICC = 0.885）。CT-COPD与VQAS中位数（中位数R1-R2）之间的一致性中等（ICC = 0.622）。(4) 结论：本研究显示VQAS和SBQAS具有中等和良好的一致性；将这种方法作为管理COVID-19患者的有价值工具加以改进，以及将人工智能工具与医生专业知识相结合，可为患者带来最准确的诊断和治疗方案。
