NLM Malaria Screener

Novel automated digital malaria diagnostic tests are being developed with the advancement of diagnostic tools. Whilst these tools are being evaluated and implemented in the general population, there is the need to focus on special populations such as individuals with Sickle Cell Disease (SCD) who have altered red blood cell morphology and atypical immune responses, which can obscure parasite detection. This study aimed to evaluate the diagnostic performance of one of such tools, the National Library of Medicine (NLM) malaria screener app in people living with sickle cell disease in a malaria-endemic country, Ghana. A descriptive cross-sectional study was conducted among SCD patients attending the Sickle Cell Clinic at Korle Bu Teaching Hospital in Accra, Ghana. Following informed consent, whole blood samples were collected and analyzed using the NLM malaria screener app, conventional microscopy, RDT, and Polymerase Chain Reaction (PCR), with PCR as the reference standard. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each diagnostic method were compared against PCR results. The NLM app identified the highest number of positive malaria cases, with 110 positive cases (36.2%), while both RDT and microscopy reported the highest number of negatives, with 287 negative cases (94.4%). Compared to PCR, the NLM app demonstrated a sensitivity of 89.5% and a specificity of 67.4%. RDT and microscopy displayed the same sensitivity as the NLM app, each achieving 89.5%. However, while RDT and microscopy had a specificity of 100%, the NLM app had a considerably lower specificity of 67.4%.The NLM malaria screener app shows promise as a preliminary screening tool for malaria in individuals with SCD. However, its lower specificity indicates a need for confirmatory testing to avoid potential overdiagnosis and mismanagement. Enhancements in the app's specificity could further support its utility in rapid and accessible malaria diagnosis for people with SCD, aiding in timely management and treatment.

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. 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 Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net. Malaria Screener reached 74.1% (95% CI 63.5–83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0–81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8–96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0–88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6–86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development. Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.