CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.

Background: Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored.

[Evaluation of a change in the serological diagnostic algorithm for Chagas disease in ColombiaAvaliação da mudança do algoritmo de diagnóstico sorológico da doença de Chagas na Colômbia].

Objective: To evaluate the effects of changing the algorithm for serological diagnosis of infection in departmental-level public health laboratories and in the National Reference Laboratory of Colombia, from the perspective of access to diagnosis.

Methods: A descriptive, cross-sectional study was carried out, based on secondary sources between 2015 and 2021, consolidating the number of serological tests carried out by the laboratories. A survey was developed to identify benefits and limitations in the implementation of the new algorithm for serological diagnosis.

Image features for quality analysis of thick blood smears employed in malaria diagnosis.

Background: The World Health Organization (WHO) provides protocols for the diagnosis of malaria. One of them is related to the staining process of blood samples to guarantee the correct parasite visualization. Ensuring the quality of the staining procedure on thick blood smears (TBS) is a difficult task, especially in rural centres, where there are factors that can affect the smear quality (e.