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Objectives To examine the radiological patterns specifically associated with hypoxemic respiratory failure in patients with coronavirus disease (COVID-19). Methods We enrolled patients with COVID-19 confirmed by qPCR in this prospective observational cohort study. We explored the association of clinical, radiological, and microbiological data with the development of hypoxemic respiratory failure after COVID-19 onset. Semi-quantitative CT scores and dominant CT patterns were retrospectively determined for each patient. The microbiological evaluation included checking the SARS-CoV-2 viral load by qPCR using nasal swab and serum specimens. Results Of the 214 eligible patients, 75 developed hypoxemic respiratory failure and 139 did not. The CT score was significantly higher in patients who developed hypoxemic respiratory failure than in those did not (median [interquartile range]: 9 [6–14] vs 0 [0–3]; p < 0.001). The dominant CT patterns were subpleural ground-glass opacities (GGOs) extending beyond the segmental area ( n = 44); defined as “extended GGOs.” Multivariable analysis showed that hypoxemic respiratory failure was significantly associated with extended GGOs (odds ratio [OR] 29.6; 95% confidence interval [CI], 9.3–120; p < 0.001), and a CT score > 4 (OR 12.7; 95% CI, 5.3–33; p < 0.001). The incidence of RNAemia was significantly higher in patients with extended GGOs (58.3%) than in those without any pulmonary lesion (14.7%; p < 0.001). Conclusions Extended GGOs along the subpleural area were strongly associated with hypoxemia and viremia in patients with COVID-19. Key Points • Extended ground-glass opacities (GGOs) along the subpleural area and a CT score > 4, in the early phase of COVID-19, were independently associated with the development of hypoxemic respiratory failure. • The absence of pulmonary lesions on CT in the early phase of COVID-19 was associated with a lower risk of developing hypoxemic respiratory failure. • Compared to patients with other CT findings, the extended GGOs and a higher CT score were also associated with a higher incidence of RNAemia. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-023-09427-0.
Objectives To examine the radiological patterns specifically associated with hypoxemic respiratory failure in patients with coronavirus disease (COVID-19). Methods We enrolled patients with COVID-19 confirmed by qPCR in this prospective observational cohort study. We explored the association of clinical, radiological, and microbiological data with the development of hypoxemic respiratory failure after COVID-19 onset. Semi-quantitative CT scores and dominant CT patterns were retrospectively determined for each patient. The microbiological evaluation included checking the SARS-CoV-2 viral load by qPCR using nasal swab and serum specimens. Results Of the 214 eligible patients, 75 developed hypoxemic respiratory failure and 139 did not. The CT score was significantly higher in patients who developed hypoxemic respiratory failure than in those did not (median [interquartile range]: 9 [6–14] vs 0 [0–3]; p < 0.001). The dominant CT patterns were subpleural ground-glass opacities (GGOs) extending beyond the segmental area ( n = 44); defined as “extended GGOs.” Multivariable analysis showed that hypoxemic respiratory failure was significantly associated with extended GGOs (odds ratio [OR] 29.6; 95% confidence interval [CI], 9.3–120; p < 0.001), and a CT score > 4 (OR 12.7; 95% CI, 5.3–33; p < 0.001). The incidence of RNAemia was significantly higher in patients with extended GGOs (58.3%) than in those without any pulmonary lesion (14.7%; p < 0.001). Conclusions Extended GGOs along the subpleural area were strongly associated with hypoxemia and viremia in patients with COVID-19. Key Points • Extended ground-glass opacities (GGOs) along the subpleural area and a CT score > 4, in the early phase of COVID-19, were independently associated with the development of hypoxemic respiratory failure. • The absence of pulmonary lesions on CT in the early phase of COVID-19 was associated with a lower risk of developing hypoxemic respiratory failure. • Compared to patients with other CT findings, the extended GGOs and a higher CT score were also associated with a higher incidence of RNAemia. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-023-09427-0.
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool for differentiating COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately detect myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.
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