Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs’ Parenchymal Involvement Quantification in COVID-19 Patients
Marco Nicolò,
Altin Adraman,
Camilla Risoli,
Anna Menta,
Francesco Renda,
Michele Tadiello,
Sara Palmieri,
Marco Lechiara,
Davide Colombi,
Luigi Grazioli,
Matteo Pio Natale,
Matteo Scardino,
Andrea Demeco,
Ruben Foresti,
Attilio Montanari,
Luca Barbato,
Mirko Santarelli,
Chiara Martini
Affiliations
Marco Nicolò
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy
Altin Adraman
Department of Neuroradiology, University Hospital of Padova, Via Giustiniani 2, 35128 Padova, Italy
Camilla Risoli
Department of Radiological Function, “Guglielmo da Saliceto” Hospital, Via Taverna 49, 29121 Piacenza, Italy
Anna Menta
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy
Francesco Renda
Department of Radiology—Diagnostic Imaging, ASST Rhodense, Viale Forlanini 95, 20024 Garbagnate Milanese, Italy
Michele Tadiello
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy
Sara Palmieri
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy
Marco Lechiara
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy
Davide Colombi
Department of Radiological Function, “Guglielmo da Saliceto” Hospital, Via Taverna 49, 29121 Piacenza, Italy
Luigi Grazioli
Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy
Matteo Pio Natale
Department of Respiratory Disease, University of Foggia, Via Antonio Gramsci 89, 71122 Foggia, Italy
Matteo Scardino
Department of Radiology, A.O.U. Città della Salute e della Scienza di Torino, Via Zuretti 29, 10126 Torino, Italy
Andrea Demeco
Department of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy
Ruben Foresti
Department of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy
Attilio Montanari
Diagnostics for Images Unit and Interventional Radiology, AST Pesaro Urbino, Piazzale Cinelli 1, 61121 San Salvatore, Italy
Luca Barbato
Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189 Rome, Italy
Mirko Santarelli
Medical Physics Unit, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189 Rome, Italy
Chiara Martini
Department of Medicine and Surgery, University of Parma, Via Gramsci 14, 43126 Parma, Italy
(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.
