A novel integrated molecular and serological analysis method to predict new cases of leprosy amongst household contacts.

ABSTRACT:

Background

Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to be

SUBMITTER: Gama RS 

PROVIDER: S-EPMC6586366 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature