Diseases

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Dear Colleagues,

The emergence of novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in late December 2019 has resulted in a pandemic, and the whole world is still struggling with the infectious viral disease called COVID-19. Within a short span of time, SARS-CoV-2 has severely challenged the entire health systems in most countries in the world with an enhanced requirement for a very specific, rapid, and early diagnosis of the infected human populations. COVID-19 has demonstrated a wide spectrum of clinical manifestations, ranging from asymptomatic to severe infections followed by acute respiratory distress syndrome and multiorgan failure leading to death. Despite considerable research efforts, the early and accurate detection of rapidly evolving variants of SARS-CoV-2 remains a challenge due to limited resources and currently available diagnostic testing for COVID-19.

Currently, the most common diagnostic methods for SARS-CoV-2 infection are based on either the detection of virus-specific nucleotides or virus-specific antigens and immunoglobulins. As of now, a number of SARS-CoV-2 serodiagnostic tests detecting specific antibodies against SARS-CoV-2 antigens have been developed. The plasma levels of pro-inflammatory and anti-inflammatory cytokines have also been shown to be strongly correlated with the progression and severity of COVID-19. Identifying such humoral immune responses, inflammatory markers, and the detection of virus-specific antigens and antibodies might add important diagnostic values to the assessment and management of SARS-CoV-2 infection. An alternative rapid, sensitive, specific, and more effective serological and immunological diagnosis of SARS-CoV-2 will be a key step to stopping the further spread of the disease.

This Special Issue of MPDI's Disease and Vaccines focuses on the recent advances and future prospects in the field of SARS-CoV-2 diagnosis. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Rapid, sensitive, specific, and scalable diagnosis and monitoring of SARS-CoV-2 infections.
Limitations of current diagnostic testing and recent developments in the field of SARS-CoV-2 diagnosis to identify specific variants of concern.
Serodiagnostic tests and their effectiveness for diagnosing SARS-CoV-2 infection
The role of antibodies in SARS-CoV-2 diagnosis
Plasma levels of pro-inflammatory and anti-inflammatory cytokines associated with the progression and severity of SARS-CoV-2 infection.
Underlying host immune responses against SARS-CoV-2 infection.
Specific immunological responses and inflammatory markers distinguishing SARS-CoV-2 infection from clinically similar non-COVID-19 viral infections.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Vaccines.

Dr. Rajiv Pathak
Dr. Prajna Tripathi
Guest Editors

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Keywords

severe acute respiratory syndrome coronavirus 2
SARS-CoV-2
COVID-19
diagnosis
immunological responses and Inflammatory markers
pro-inflammatory and anti-inflammatory cytokines
virus-specific antigens and antibodies
host immune responses

Published Papers (1 paper)

Research

13 pages, 2437 KiB

Open AccessArticle

Artificial Intelligence Applied to Chest X-ray: A Reliable Tool to Assess the Differential Diagnosis of Lung Pneumonia in the Emergency Department

by Davide Ippolito, Cesare Maino, Davide Gandola, Paolo Niccolò Franco, Radu Miron, Vlad Barbu, Marco Bologna, Rocco Corso and Mihaela Elena Breaban

Diseases 2023, 11(4), 171; https://doi.org/10.3390/diseases11040171 - 20 Nov 2023

Cited by 1 | Viewed by 2038

Abstract

Background: Considering the large number of patients with pulmonary symptoms admitted to the emergency department daily, it is essential to diagnose them correctly. It is necessary to quickly solve the differential diagnosis between COVID-19 and typical bacterial pneumonia to address them with the best management possible. In this setting, an artificial intelligence (AI) system can help radiologists detect pneumonia more quickly. Methods: We aimed to test the diagnostic performance of an AI system in detecting COVID-19 pneumonia and typical bacterial pneumonia in patients who underwent a chest X-ray (CXR) and were admitted to the emergency department. The final dataset was composed of three sub-datasets: the first included all patients positive for COVID-19 pneumonia (n = 1140, namely “COVID-19+”), the second one included all patients with typical bacterial pneumonia (n = 500, “pneumonia+”), and the third one was composed of healthy subjects (n = 1000). Two radiologists were blinded to demographic, clinical, and laboratory data. The developed AI system was used to evaluate all CXRs randomly and was asked to classify them into three classes. Cohen’s κ was used for interrater reliability analysis. The AI system’s diagnostic accuracy was evaluated using a confusion matrix, and 95%CIs were reported as appropriate. Results: The interrater reliability analysis between the most experienced radiologist and the AI system reported an almost perfect agreement for COVID-19+ (κ = 0.822) and pneumonia+ (κ = 0.913). We found 96% sensitivity (95% CIs = 94.9–96.9) and 79.8% specificity (76.4–82.9) for the radiologist and 94.7% sensitivity (93.4–95.8) and 80.2% specificity (76.9–83.2) for the AI system in the detection of COVID-19+. Moreover, we found 97.9% sensitivity (98–99.3) and 88% specificity (83.5–91.7) for the radiologist and 97.5% sensitivity (96.5–98.3) and 83.9% specificity (79–87.9) for the AI system in the detection of pneumonia+ patients. Finally, the AI system reached an accuracy of 93.8%, with a misclassification rate of 6.2% and weighted-F1 of 93.8% in detecting COVID+, pneumonia+, and healthy subjects. Conclusions: The AI system demonstrated excellent diagnostic performance in identifying COVID-19 and typical bacterial pneumonia in CXRs acquired in the emergency setting. Full article

(This article belongs to the Special Issue Diagnosis for SARS-CoV-2 Infections)

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