Artificial intelligence quantitative evaluation in diagnosis and grading of CTD-ILD
CHU Chen
FANG Yu
SUN Yi
WANG Chun
WEI Ying
SHI Feng
XIN Xiaoyan
ZHAO Shengnan
Abstract：Objective To explore the value of artificial intelligence in quantitative of connective tissue disease-associated in-terstitial lung disease(CTD-ILD).Methods A total of 128 CTD-ILD patients were prospectively enrolled and divided into mild and severe groups.The independent sample t test and ROC analysis were used to distinguish the parameters of pneumonia analysis in mild and severe groups.The analysis of variance and LSD test were used to compare the lesion components.The spear-man rank analysis was used to compare the correlation between all parameters and pulmonary function grades.Results The lung volume of severe groups was significantly higher than mild groups(P value were≤0.001).The ROC curves showed that the volume and percentage indexes of the lung were of high diagnostic value(AUCs>0.700).There were significantly differences be-tween different lesion components.The parameters were correlated with the pulmonary function grades.Conclusion Artificial intelligence has advantages in the quantitative analysis of CTD-ILD patients and can provide value for the diagnosis and grading of patients.
Keywords：Interstitial lung diseaseConnective tissue diseaseArtificial intelligenceTomographyX-ray computed
Publication Date：2024-08-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 54-57,61 )