首页|Xiangya Hospital of Central South University Reports Findings in Artificial Inte lligence (Use of artificial intelligence algorithms to analyse systemic sclerosi s-interstitial lung disease imaging features)
Xiangya Hospital of Central South University Reports Findings in Artificial Inte lligence (Use of artificial intelligence algorithms to analyse systemic sclerosi s-interstitial lung disease imaging features)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news originating from Changsha, People’s Republi c of China, by NewsRx correspondents, research stated, “The use of artificial in telligence (AI) in high-resolution computed tomography (HRCT) for diagnosing sys temic sclerosisassociated interstitial lung disease (SSc-ILD) is relatively lim ited. This study aimed to analyse lung HRCT images of patients with systemic scl erosis with interstitial lung disease (SSc-ILD) using artificial intelligence (A I), conduct correlation analysis with clinical manifestations and prognosis, and explore the features and prognosis of SSc-ILD.” Our news journalists obtained a quote from the research from the Xiangya Hospita l of Central South University, “Overall, 72 lung HRCT images and clinical data o f 58 patients with SSC-ILD were collected. ILD lesion type, location, and volume on HRCT images were identified and evaluated using AI. The imaging characterist ics of diffuse SSC (dSSc)-ILD and limited SSc-ILD (lSSc-ILD) were statistically analysed. Furthermore, the correlations between lesion type, clinical indicators , and prognosis were investigated. dSSc and lSSc were more prevalent in patients with a disease duration of <1 and 5 years, respectively. SSc-ILD mainly comprises non-specific interstitial pneumonia (NSIP), usual inter stitial pneumonia (UIP), and unclassifiable idiopathic interstitial pneumonia. H RCT reveals various lesion types in the early stages of the disease, with an inc rease in the number of lesion types as the disease progresses. Lesions appearing as grid, ground-glass, and nodular shadows were dispersed throughout both lungs , while those appearing as consolidation shadows and honeycomb were distributed across the lungs. Ground-glass opacity lesion type was absent on HRCT images of patients with SSc-ILD and pulmonary hypertension.”
ChangshaPeople’s Republic of ChinaAs iaAlgorithmsArtificial IntelligenceEmerging TechnologiesHealth and Medic ineInfectious DiseaseInterstitial Lung DiseaseLung Diseases and ConditionsMachine LearningPneumoniaPulmonologyRespiratory Tract Diseases and Condi tionsRespiratory Tract Infections
NETL
NSTL
