Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy

Castiglioni, Isabella; Ippolito, Davide; Interlenghi, Matteo; Monti, Caterina Beatrice; Salvatore, Christian; Schiaffino, Simone; Polidori, Annalisa; Gandola, Davide; Messa, Cristina; Sardanelli, Francesco

doi:10.1101/2020.04.08.20040907

Cited by 74 publications

(69 citation statements)

References 8 publications

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“…As described by Rijn et al[ 64 ], following an initial time investment by a radiologist to train a deep learning system, tasks such as image segmentation can be performed with speed and accuracy[ 64 ]. Articles in preprint archives describe training and testing of convolutional neural networks on CXR[ 65 , 66 ]. Deep learning algorithms based upon 2D and 3D learning models on non-contrast chest CT are being developed to aid in detection, quantification and analysis of progression in COVID-19 and are also found in print and in preprint repositories[ 67 - 69 ].…”

Section: Role Of Artificial Intelligencementioning

confidence: 99%

Review of radiographic findings in COVID-19

Kaufman

Naidu

Ramachandran

et al. 2020

WJR

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The purpose of this study is to review the published literature for the range of radiographic findings present in patients suffering from coronavirus disease 2019 infection. This novel corona virus is currently the cause of a worldwide pandemic. Pulmonary symptoms and signs dominate the clinical picture and radiologists are called upon to evaluate chest radiographs (CXR) and computed tomography (CT) images to assess for infiltrates and to define their extent, distribution and progression. Multiple studies attempt to characterize the disease course by looking at the timing of imaging relative to the onset of symptoms. In general, plain CXR show bilateral disease with a tendency toward the lung periphery and have an appearance most consistent with viral pneumonia. Chest CT images are most notable for showing bilateral and peripheral ground glass and consolidated opacities and are marked by an absence of concomitant pulmonary nodules, cavitation, adenopathy and pleural effusions. Published literature mentioning organ systems aside from pulmonary manifestations are relatively less common, yet present and are addressed in this review. Similarly, publications focusing on imaging modalities aside from CXR and chest CT are sparse in this evolving crisis and are likewise addressed in this review. The role of imaging is examined as it is currently being debated in the medical community, which is not at all surprising considering the highly infectious nature of Severe Acute Respiratory Syndrome coronavirus 2.

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Section: Role Of Artificial Intelligencementioning

confidence: 99%

Review of radiographic findings in COVID-19

Kaufman

Naidu

Ramachandran

et al. 2020

WJR

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“…In addition, artificial intelligence can further enhance the potential of this setting: convolutional neural networks have been shown to support the diagnosis of COVID-19 [9], so that the reporting radiologist could have a validated machine/deep learning second opinion to consider. This could be extremely useful in the current scenario of rapid change of COVID-19 prevalence, continuously modifying not only predictive values, but also sensitivity and specificity of diagnostic tests [10].…”

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confidence: 99%

Bringing radiology to patient's home using mobile equipment: A weapon to fight COVID-19 pandemic

2020

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Because of coronavirus disease 2019 (COVID-19) high contagiousness, it is crucial to identify and promptly isolate COVID-19 patients. In this context, chest imaging examinations, in particular chest x-ray (CXR), can play a pivotal role in different settings, to triage in case of unavailability, delay of or first negative result of reverse transcriptase-polymerase chain reaction (RT-PCR), and to stratify disease severity. Considering the need to reduce, as much as possible, hospital admission of patients with suspected or confirmed infection, the use of mobile x-ray equipment could represent a safe approach. We picture a potential sequence of events, involving a team composed by a radiographer and a nurse, going to patient's home to perform CXR, nasopharyngeal swab (and, if needed, also a blood sample), with fast radiologist tele-reporting, and resulting patient management approach (home isolation or emergency room admission, when needed). This approach brings healthcare to patient's home, reducing the risk of infected subjects referring to family doctors' office or emergency departments, and strengthening community medicine while maintaining a strong connection with radiology departments.

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“…Due to the wide availability of conventional chest radiography units especially the mobile ones and the ease of decontaminating the equipment between patients has given CXR an essential role in the fight against this pandemic. In order to support radiologist to perform such tasks, some research groups have developed Deep learning /Transfer learning techniques to analyse CXRs and classify them into COVID and non-COVID (normal CXR), however these algorithms have achieved varying results [12], [13].…”

Section: Related Workmentioning

confidence: 99%

AI based Chest X-Ray (CXR) Scan Texture Analysis Algorithm for Digital Test of COVID-19 Patients

Al-karawi¹,

Al-Zaidi²,

Polus³

et al. 2020

Preprint

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Chest Imaging in COVID-19 patient management is becoming an essential tool for controlling the pandemic that is gripping the international community. It is already indicated in patients with COVID-19 and worsening respiratory status. The rapid spread of the pandemic to all continents, albeit with a nonuniform community transmission, necessitates chest imaging for medical triage of patients presenting moderate-severe clinical COVID-19 features. This paper reports the development of innovative machine learning schemes for the analysis of Chest X-Ray (CXR) scan images of COVID-19 patients in almost real-time, demonstrating significantly high accuracy in identifying COVID-19 infection. The performance testing was conducted on a combined dataset comprising CXRs of positive COVID-19 patients, patients with various viral and bacterial infections, as well as persons with a clear chest. The test resulted in successfully distinguishing CXR COVID-19 infection from the other cases with an average accuracy of 94.43%, sensitivity 95% and specificity 93.86%.

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Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy

Cited by 74 publications

References 8 publications

Review of radiographic findings in COVID-19

Review of radiographic findings in COVID-19

Bringing radiology to patient's home using mobile equipment: A weapon to fight COVID-19 pandemic

AI based Chest X-Ray (CXR) Scan Texture Analysis Algorithm for Digital Test of COVID-19 Patients

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