Differential Diagnosis of Tuberculosis and Sarcoidosis by Immunological Features Using Machine Learning.

ABSTRACT: Despite the achievements of modern medicine, tuberculosis remains one of the leading causes of mortality globally. The difficulties in differential diagnosis have particular relevance in the case of suspicion of tuberculosis with other granulomatous diseases. The most similar clinical and radiologic changes are sarcoidosis. The aim of this study is to apply mathematical modeling to determine diagnostically significant immunological parameters and an algorithm for the differential diagnosis of tuberculosis and sarcoidosis. Materials and methods: The serum samples of patients with sarcoidosis (SD) (n = 29), patients with pulmonary tuberculosis (TB) (n = 32) and the control group (n = 31) (healthy subjects) collected from 2017 to 2022 (the average age 43.4 ± 5.3 ye

SUBMITTER: Osipov N 

PROVIDER: S-EPMC11476257 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature