Objective: To explore the application value of artificial intelligence (AI) in quantitative evaluation of CT images of different clinical types of COVID-19. Methods: A total of 169 patients with COVID-19 who underwent chest CT examination between October 2021 and June 2022 were included in the study, and were divided into a severe group of 14 cases and a non-severe group of 155 cases. The general data and imaging information of the COVID-19 patients were collected and analyzed retrospectively. The lesion volume fraction, GGO volume fraction, consolidation volume fraction, GGO + consolidation volume fraction and crazy-paving pattern volume fraction were measured and calculated with the assistance of the “AI qualitative auxiliary diagnosis system for CT images of COVID-19” provided by BIOMIND. Mann-whitney U test was used to compare the data of the two groups. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of the above indexes in severe patients. Results: There were significant differences in lesion volume fraction and GGO volume fraction between the two groups (P＜0.05). However, there was no significant difference in the volume fraction of consolidation, GGO + consolidation and crazy-paving pattern (P＞0.05). The sensitivity of lesion volume fraction and GGO volume fraction for clinical classification of COVID-19 was 1 and 0.6, and the specificity was 0.822 and 0.923, respectively. Conclusion: The lesion volume fraction and GGO volume fraction calculated with the assistance of AI are sensitive indicators for clinical classification of COVID-19.
WANG Haijing
,
CHEN Qiang
,
LUO Lin
. Application value of AI-assisted lesion volume assessment in clinical classification of COVID-19[J]. Journal of Baotou Medical College, 2024
, 40(4)
: 38
-41
.
DOI: 10.16833/j.cnki.jbmc.2024.04.007
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