Patient-level performance evaluation of a smartphone-based malaria diagnostic application.

ABSTRACT:

Background

Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis.

Methods

A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria

SUBMITTER: Yu H 

PROVIDER: S-EPMC9883923 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature