Diagnostic tools, curated datasets and health-systems research — developed at Makerere University and validated in the clinical settings they are built for.
Ocular's 3D printable adapter connects smartphones to microscopes, allowing for easy image capture and alignment in health centers. This technology enhances pathogen identification for diseases like malaria and tuberculosis, supported by a mobile and web application for diagnostic assistance.
We are developing a rapid, low-cost screening system for malaria diagnosis using a 3D printed smartphone adapter for microscopes and deep learning models, aimed at enhancing real-time diagnostics and surveillance in Uganda to improve public health outcomes.
The Lacuna Fund project aims to enhance malaria microscopy diagnosis in low and middle-income settings by generating high-quality, open-labeled blood smear datasets from Uganda and Ghana, using smartphone-mounted microscopes to support machine learning models for detection.
This project aims to enhance cervical cancer detection in Sub-Saharan Africa by curating a labeled digital dataset of 1,000 Pap smear images using a smartphone-mounted microscope, supporting machine learning tasks for early and reliable screening.
We propose an AI-powered ultrasound screening tool to detect high-risk pregnancies early, aiming to reduce maternal mortality in Uganda and similar settings. The solution will integrate clinical, demographic, and imaging data to support timely and accurate diagnosis at the primary healthcare level.
This project aims to support natural resource management in Uganda by using AI to analyze satellite and forestry inventory data for predicting land cover changes and improving decision-making in response to deforestation and climate change.
Several of our projects exist primarily to produce openly-licensed, expertly-labelled health datasets from African clinical settings.
The Lacuna Fund project aims to enhance malaria microscopy diagnosis in low and middle-income settings by generating high-quality, open-labeled blood smear datasets from Uganda and Ghana, using smartphone-mounted microscopes to support machine learning models for detection.
This project aims to enhance cervical cancer detection in Sub-Saharan Africa by curating a labeled digital dataset of 1,000 Pap smear images using a smartphone-mounted microscope, supporting machine learning tasks for early and reliable screening.
This project aims to support natural resource management in Uganda by using AI to analyze satellite and forestry inventory data for predicting land cover changes and improving decision-making in response to deforestation and climate change.
Access terms differ by dataset, depending on the consent basis and the agreements with the institutions where the data was collected. Tell us which dataset you are interested in and what you intend to use it for, and we will confirm what is available and under what licence.
We work with clinical partners, funders, and researchers who want to collaborate on or extend this work.