Participants will review clinical lesion photographs, outline lesion borders, record morphology according to a structured vocabulary, identify inadequate photographs, and flag ambiguous cases for dermatology or tropical-medicine adjudication.
Expected outputs include:
Curated skin-lesion dataset.
Segmentation masks.
Standardized morphological labels.
CL versus comparator labels.
Inter-rater analysis.
Candidate screening model.
Sensitivity, specificity, AUROC and Dice/IoU analyses.
Manuscript, poster, certificates.
Artificial Intelligence in Healthcare
Artificial Intelligence, Dermatology, Parasitology, Infectious Diseases, Tropical 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: Leal JFC, et al. Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence. Biomedicines. 2024;12:12. PMID: 38275373. The study used 2,458 photographs and differentiated CL from numerous other dermatoses.
- Training will emphasize cutaneous leishmaniasis morphology, differential visual patterns, photographic standardization, lesion segmentation, annotation reliability, and the limitations of image-only screening.
October 15, 2026 at 12:00:00 AM
October 20, 2026
October 26, 2026
December 15, 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.