Rapid and accurate recognition of erythrocytic stage parasites of <i>Plasmodium falciparum</i> via a deep learning-based YOLOv3 platform.

ABSTRACT:

Background

Malaria remains a fatal global infectious disease, with the erythrocytic stage of Plasmodium falciparum being its main pathogenic phase. Early diagnosis is critical for effective treatment. This study developed and evaluated an artificial intelligence-assisted diagnosis (AI-assisted diagnostic) tool for malaria parasites.

Methods

The peripheral blood samples of malaria patients were collected. Thin blood film smear were prepared, stained and examined by microscopic. After manual confirmation and validation with qPCR, the images of infected red blood cells (iRBCs) of P. falciparum were captured. Using a sliding window method, each original image was cropped into 20 small images (518 × 486 pixels). Selected iRBCs were classified, and P. falciparum</

SUBMITTER: He W 

PROVIDER: S-EPMC12611930 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature