Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning.

ABSTRACT:

Background

Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination.

Methods

We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection.

Findings

Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low dens

SUBMITTER: Sikulu-Lord MT 

PROVIDER: S-EPMC10962802 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature