Participants will annotate parasite-positive regions, visible parasitic structures, developmental forms, species categories where supportable, artifacts, and uncertain cases. They will conduct independent second-pass review and participate in specialist adjudication.
They will also help curate the microscopy dataset, review model errors, contribute to manuscript sections, and prepare conference materials.
Expected outputs include:
Curated malaria microscopy dataset.
Parasite localization annotations.
Species and morphology labels.
Artifact/indeterminate labels.
Inter-rater agreement analysis.
Candidate parasite-detection and species-classification model.
Parasite-level precision/recall analysis.
Specimen-level performance assessment.
Manuscript and international scientific poster.
Individualized microscopIA certificates.
Artificial Intelligence in Healthcare
Artificial Intelligence, Parasitology, Infectious Diseases, Laboratory Medicine
Jorge Racedo at microscopIA, External Clinicians
Open internationally to medical students, health-science students, physicians, biomedical engineers, data-science students, laboratory professionals, and other students or professionals whose backgrounds are relevant to the project.
Prior experience in artificial intelligence is not required. Participants will receive project-specific training before independent annotation begins.
Applicants should have:
Professional or academic interest in the clinical area.
Sufficient English proficiency for scientific work.
Reliable internet access.
Strong attention to detail.
Ability to follow standardized research protocols.
Availability for approximately 3–5 hours per week.
Willingness to participate in scientific writing and quality-control activities.
Participation is offered through microscopIA membership. Selected applicants must hold a membership tier that permits enrollment in Join a Project and comply with the applicable Membership and Project Terms throughout their participation.
Any Country
Clinical Annotation, Data Curation, Data Validation, Data Quality Control, Literature Review, Database Development, Manuscript Preparation, Manuscript Revision, Poster Preparation, Conference Presentation, Response to Reviewers, AI Training (Data Processing and Labeling)
7
English, Spanish
External PubMed-indexed methodological precedent: Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears. PMID: 37937045. The study developed a deep-learning system specifically for identifying and classifying malaria parasites from clinical thin blood smears.
- Training will cover blood-smear morphology, common Plasmodium species features, thick versus thin smear interpretation, microscopy artifacts, labeling rules, image quality, annotation tools, uncertainty handling, and quality control. Experienced parasitologists or microscopists will adjudicate clinical labels. Laboratory species confirmation will be incorporated where available.
15 de octubre de 2026 a las 0:00:00
20 de octubre de 2026
26 de octubre de 2026
15 de diciembre de 2026
Each participant may declare a maximum of two institutional affiliations.
Format:
Department, Organization (Hospital, University, Company), City, Country.microscopIA will cover approved publication fees associated with the anticipated regional manuscript.
Participants will not be required to personally pay approved article-processing charges.
Memberships are reinvested to pay microscopIA staff (epidemiologists, engineers, data scientists) salaries and to cover the publication fees for the resulting manuscripts. This maintain our work neutral, financially self-sustainable, and without any existing conflict of interest to maintain rigor and integrity in evidence generation.