Chest computed tomography in COVID-19 infection

Crimì, Filippo; Cabrelle, Giulio; Zanon, Chiara; Quaia, Emilio

doi:10.1007/s40336-021-00455-8

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…The novel coronavirus was named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses and led to a dramatic pneumonia outbreak in China [1][2][3]. The disease caused by the virus, named coronavirus disease (COVID- 19) by the World Health Organization (WHO), can be spread through human-to-human contact. On January 30, 2020, the WHO declared a global public health emergency against the outbreak of COVID-19 [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The COVID-19 diagnosis is confirmed by the positive results of the nucleic acid amplification test of the respiratory tract or blood specimens using reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) [7,8]. However, methods like chest X-ray (CXR) and chest Computed Tomography (CT) scan are medical imaging techniques, which are widely used to assess the pneumonia due to COVID-19 [9][10][11][12][13][14][15][16][17][18][19]. The reported sensitivity of CXR for COVID-19 pneumonia is relatively low in the early phase of the disease and in mild cases (69%).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

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Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4–99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0–99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0–100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

View full text Add to dashboard Cite

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“…We examined the radiation exposure faced by COVID-19 patients in ICUs [6], [7], the risks associated with repetitive imaging in ICU patients [8], and varying degrees of radiation exposure in different patient populations. [2], [9], [10] The findings of this review emphasize the need for a meticulous approach to radiological practice, guided by the principles of justification, optimization, and strict adherence to dose limits, as advocated by the International Commission on Radiological Protection. [11], [12]Striking the delicate equilibrium between the benefits of diagnostic imaging and the potential hazards of radiation exposure is a continuous challenge, particularly when caring for critically ill adults.…”

Section: Introductionmentioning

confidence: 99%

Radiation Overuse in Intensive Care Units: A Cause for Concern

Zanon,

Bini,

Toniolo

et al. 2023

Preprint

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Radiological imaging is essential for acute patient management in Intensive Care Units (ICUs); however, it introduces the risk of exposure to ionizing radiation. This review synthesizes research on radiation exposure in intensive care unit (ICU) settings, highlighting its rise during the COVID-19 pandemic and repetitive imaging. Our analysis extends to radiation safety thresholds, revealing that some ICU patients exceed annual occupational limits, emphasizing the need to balance diagnostic utility against potential long-term risks, such as cancer. Prospective studies have demonstrated an increase in the median Cumulative Effective Dose (CED) in patients with trauma over time, prompting calls for improved awareness and monitoring. Moreover, innovative dose-reduction strategies and optimized protocols, especially in neuro-ICU units, promise to mitigate these risks. This review highlights the essential but risky role of radiological imaging in critical care. It advocates rigorous radiation management protocols to safeguard patient health, while ensuring the continuity of high-quality medical care.

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Chest computed tomography in COVID-19 infection

Cited by 2 publications

References 5 publications

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Radiation Overuse in Intensive Care Units: A Cause for Concern

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