6533b871fe1ef96bd12d260e

RESEARCH PRODUCT

A machine learning application to predict early lung involvement in scleroderma: A feasibility evaluation

Simone NegriniAlessandro TonacciGiuseppe CittadiniElvira Ventura SpagnoloLucia BilleciSimone CaprioliMonica GrecoPatrizia ZentilinSebastiano GangemiGiuseppe Murdaca

subject

Elastic net regularizationSpirometryMedicine (General)High-resolution computed tomographyArtificial intelligenceClinical BiochemistryDiseaseMachine learningcomputer.software_genreArticlePulmonary function testingR5-920Machine learningmedicineCause of deathEsophageal dilatationintegumentary systemmedicine.diagnostic_testbusiness.industryHRCT chestRegressionRandom forestArtificial intelligence; Esophageal dilatation; HRCT chest; Machine learning; Systemic sclerosisSystemic sclerosisArtificial intelligencebusinesscomputer

description

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations

yearjournalcountryeditionlanguage
2021-10-12
10.3390/diagnostics11101880http://hdl.handle.net/11570/3214019