Imaging in coronavirus disease 2019 part 4

The virus outbreak presumably originated via a zoonotic transmission linked to the seafood market in Wuhan (China) and later accelarated with human to human transmission, causing the severe subsequent outbreak. Since the original outbreak, SARS-CoV2 has rapidly spread across the world and, in January 2020, The World Health Organization (WHO) declared COVID-19 a global public health emergency. 

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Imaging in coronavirus disease 2019 part 3

In late November 2019, a serious form of pulmonary illness originated in Wuhan City (Hubei province, China) an engulfed a majority of the world. This pneumonia outbreak was attributed to a novel coronavirus, a lipid-enveloped RNA virus, which was named by the International Committee on Taxonomy of viruses severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). The disease caused by the virus was termed coronavirus disease-2019 (COVID-19).

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Imaging in coronavirus disease 2019 part 2

The limitations of RT-PCR, specifically. the fact that it is time-consuming and inadequate for the assessment of disease severity, have affected the process of epidemiological disease containment and has taken a toll on the healthcare management chain. As the risk of infection for other patients and personnel must be kept to a minimum, the indications for imaging have to be carefully considered. Imaging is primarily performed in patients with a negative RT-PCR, but high clinical suspicion of COVID-19, or, in patients with diagnosed COVID-19 who are suffering from moderate to severe symptoms.

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Imaging in coronavirus disease 2019 part 1

Coronavirus disease-2019 (COVID-19) originated in the Wuhan, Hubei Province, China in November 2019 and has since been declared a pandemic by the WHO. COVID-19 is an acute infectious disease, primarily affecting the respiratory system. Currently, real-time reverse transcription polymerase chain reaction (RT-PCR) performed on respiratory specimens is considered the the reference by which to diagnose COVID-19.

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Imaging of coronavirus disease 2019 part 26

In general, combined chest CT, clinical symptoms, and laboratory tests facilitates the diagnosis of COVID-19. Increasing in-depth understanding of the disease, research, and the continuous improvement of artificial intelligence technology will further  promote the establishment of a comprehensive prevention and control system of early screening, diagnosis, isolation, and treatment of COVID-19 pneumonia.

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Imaging of coronavirus disease 2019 part 25

Some researchers have tried to apply artificial intelligence in CT image analysis to differentiate COVID-19 from other viral pneumonia patients. With clinical symptoms, laboratory testing results, and contact or travel history, the artificial intelligence system can help doctors identify patients with risk of progressing to a more severe disease state at the time of admission, for timely, precise, and effective treatment decisions. Hence, precise lesion labelling, segmentation, and quantification analysis of COVID-19 lesions is the future of artificial intelligence.

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Imaging of coronavirus disease 2019 part 24

The artificial intelegence – system has outstanding perfomance in the detection of subtle GGO, which is the most easily missed typical CT feature of COVID-19. Also, it can precisely segment the lesion region, calculate the lesion volume, volume rates of lesions to total/left/right lung, and each in lung lobe. Comparing CT scans of the same patients at several time points, the radiologist can use the system to measuire changes in each lesion and track the progression of the disease.

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Imaging of coronavirus disease 2019 part 23

Application of artificial intelligence (AI) in COVID-19

A large amount of CT images makes it difficult for radiologists to compare among serial studies. Thus, rapid detection, accurate location of lesions, and evaluation of lesion size, properties, and lesion dynamics are urgent issues that need to be addressed. An AI-assisted diagnostic system for COVID-19 has been developed in China. It takes about 15 s with an accuracy rate above 90%.

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Imaging of coronavirus disease 2019 part 22

Streptococcus pneumonia is characterized by the consolidation of lobes or lobules without GGO. Both mycoplasma and aspiration peumonia distribute along bronchovascular bundle, which is significantly different from COVID-19 pneumonia. In study comparing chest CT from 219 patients with COVID-19 pneumonia in China and 205 patients with other cause of viral pneumonia in the United States, COVID-19 pneumonia cases were more likely to have a peripheral distribution  (80 versus 57 percent), and GGO (91 versus 68 percent). COVID-19 patients more frequently had multifocal involvement on CT, compared with unifocalinvolvement in SARS and MERS.

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Imaging of coronavirus disease 2019 part 21

Differential  diagnosis with other pneumonia

Although the imaging features of COVID-19 overlap with those of SARS and MERS, there are differences on imaging exams that set the COVID-19 pnemonia apart. It is essential to make a differential diagnosis for early identification of borderline patients and determination of the appropriate treatment. Viral  pneumonia is characterized by alveolar wall edema and interstitial changes.

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