Application of artificial intelligence in chest imaging for COVID-19

Kim, Eun Young; Chung, Myung Jin

doi:10.5124/jkma.2021.64.10.664

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…In the last decade, deep-learning-based automatic detection (DLAD) algorithms have significantly surpassed conventional computer-aided detection (CAD) systems in a variety of tasks related to chest radiographs (CXR) [ 1 , 2 , 3 , 4 ]. These advanced algorithms have seen an increased adoption in clinical settings for detecting and classifying a wide range of abnormal pulmonary lesions such as nodules, masses, and COVID-19 pneumonia [ 5 , 6 , 7 , 8 , 9 ]. They provide invaluable assistance to physicians, particularly in cases where radiologists are unavailable or overburdened [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…These advanced algorithms have seen an increased adoption in clinical settings for detecting and classifying a wide range of abnormal pulmonary lesions such as nodules, masses, and COVID-19 pneumonia [ 5 , 6 , 7 , 8 , 9 ]. They provide invaluable assistance to physicians, particularly in cases where radiologists are unavailable or overburdened [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study

Lim,

Cha,

Chung

et al. 2023

Diagnostics

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Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. Results: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm3 (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm3 m3 in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e., RMSE: 7975.6 mm3, the smallest among the other variable parameters). Conclusions: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs.

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