According to our results, voice sample analytics may be applied to as anaid to clinical screening of laryngeal/lungs tuberculosis disease, since itproduces differences in the voice parameters, as a sort of voice signature.

We evaluated the possibility that the human voice could furnish a cost-effective reliable tool in view of non-invasive safety diagnosis of human tuberculosis.

The techniques are handy and portable. Though the project is in an early stage, however, it has a potential of being reliable clinical screening technology.

Machine learning andartificial intelligence techniques are used to improvehealth results and to detect diseases at an early stage aswell as detecting patterns is also being made easy.