A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.

Publication date: May 21, 2020

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

Concepts Keywords
AI Artificial intelligence
Artificial Intelligence Health
ArXiv Tomography
AUC Tomography
Chest Radiography Pneumonia
Computed Tomography Nuclear medicine
Coronavirus Image processing
Diagnostic Imaging Medical physics
Google Medicine
Lungs Medical imaging
Pneumonia Pneumonia
PubMed Scholar search studies
Diagnostic imaging

Semantics

Type Source Name
disease MESH pneumonia

Original Article

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