COVID-19 versus H1N1: challenges in radiological diagnosis—comparative study on 130 patients using chest HRCT

Abstract

Background During the current second wave of COVID-19, the radiologists are expected to face great challenges in differentiation between COVID-19 and other virulent influenza viruses, mainly H1N1. Accordingly, this study was performed in order to find any differentiating CT criteria that would help during the expected clinical overlap during the current Influenza season. Results This study was retrospectively conducted during the period from June till November 2020, on acute symptomatic 130 patients with no history of previous pulmonary diseases; 65 patients had positive PCR for COVID-19 including 50 mild patients and 15 critical or severe patients; meanwhile, the other 65 patients had positive PCR for H1N1 including 50 mild patients and 15 critical or severe patients. They included 74 males and 56 females (56.9%:43.1%). Their age ranged 14–90 years (mean age 38.9 ± 20.3 SD). HRCT findings were analyzed by four expert consultant radiologists in consensus. All patients with COVID-19 showed parenchymal or alveolar HRCT findings; only one of them had associated airway involvement. Among the 65 patients with H1N1; 56 patients (86.2%) had parenchymal or alveolar HRCT findings while six patients (9.2%) presented only by HRCT signs of airway involvement and three patients (4.6%) had mixed parenchymal and airway involvement. Regarding HRCT findings of airway involvement (namely tree in bud nodules, air trapping, bronchial wall thickening, traction bronchiectasis, and mucous plugging), all showed significant p value (ranging from 0.008 to 0.04). On the other hand, HRCT findings of parenchymal or alveolar involvement (mainly ground glass opacities) showed no significant relation. Conclusion HRCT can help in differentiation between non-severe COVID-19 and H1N1 based on signs of airway involvement.

Comparison of Chest CT Findings between COVID-19 Pneumonia and Influenza Pneumonia

“…Сумасшедшая исчерченность и ретикулярные изменениями более характерны для COVID-19, они указывают на более позднюю стадию заболевания [14,15]. У пациентов с тяжелым COVID-19 и гриппом (преимущественно это пациенты с ДАП и ОРДС) отмечаются диффузные затемнения по типу матового стекла и изменения в виде заполненных воздухом кист, а результаты КТВР, свидетельствующие о поражениях паренхимы или альвеол, не позволяют провести разграничение между ними [16].…”

Classificação e Segmentação de COVID-19 em Imagens de Tomografia Computadorizada Usando Aprendizado Profundo

“…A COVID-19 é uma doenc ¸a causada pelo novo coronavírus (SARS-COV-2), essa doenc ¸a é altamente infecciosa com diversos sintomas que são facilmente confundidos com outras doenc ¸as, tais como a pneumonia viral ou bacteriana [Samir et al 2021]. A COVID-19 apresenta diversos sintomas que podem variar em cada paciente, podendo apresentar quadros clínicos leves e recuperac ¸ões rápidas.…”

Segmentation of COVID-19 lesions on computed tomography images: An approach using U-Net and pre-processing techniques

“…COVID-19 is a disease caused by the novel coronavirus (SARS-COV-2). This disease is highly infectious, with several symptoms that can be confused with other diseases, such as pneumonia caused by viruses or bacteria [1]. COVID-19 has several symptoms that may vary for each patient, presenting mild clinical conditions and rapid recoveries.…”