Approved Research This page provides a searchable list of all research protocols that have been reviewed and approved by the Uganda National Council for Science and Technology(UNCST).
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Name Title Nationality Approval Date Expiry Date Field of Science/Classification Trial Type Research Type  
Nalwanga Lillian Edith
ID: UNCST-2025-R022342
 HYPOXIC ISCHAEMIC ENCEPHALOPATHY CASEFATALITY, AND ITS ASSOCIATED FACTORS AMONG NEONATES AT KAYUNGA REGIONAL REFERRAL HOSPITAL
REFNo: HS7419ES

Main Objective 1. To determine the case fatality rate and its associated factors among neonates diagnosed with hypoxic-ischaemic encephalopathy at Kayunga Regional Referral Hospital. Specific Objectives 1. To determine the case fatality rate among neonates diagnosed with hypoxic-ischaemic encephalopathy at Kayunga Regional Referral Hospital between 1st January 2022 to 31st December 2025. 2. To identify maternal and intrapartum factors associated with hypoxic-ischaemic encephalopathy at Kayunga Regional Referral Hospital. 3. To identify neonatal factors associated with the hypoxic-ischaemic encephalopathy case fatality rate at Kayunga Regional Referral Hospital.
 Uganda 2026-04-27 11:40:12 2029-04-27 Medical and Health Sciences Non-Clinical Trial Degree Award
Andrew Katumba
ID:
Machine Learning-based Algorithm for Automatic Screening of Pulmonary Tuberculosis using Chest X-rays in the Ugandan Population
REFNo: HS7323ES

1.Create an open, labelled, inclusive dataset for CXR images, clinical and lab information collected from the Ugandan population,
2.Develop machine learning models for automatic recognition of TB features in CXR images,
3.Integrate the developed models in a decision support web application,
4.Calibrate and validate the developed platform within Ugandan clinical settings.

 Uganda 2026-04-27 11:37:57 2029-04-27 Medical and Health Sciences Non-Clinical Trial Non-degree Award
Moses Okech
ID: UNCST-2024-R015996
 African Youth Panel: An investigation into the factors hindering youth from accessing dignified and fulfilling work in some communities in Uganda
REFNo: SS5046ES

Aim: To achieve a demonstrable impact on advancing access to employment and dignified and fulfilling work for young people, with a specific focus on young women, in the selected project countries.
Key Expected Outcomes (Objectives):
Objective Description
Social and Economic Opportunity An increase in social and economic opportunities for young people (the majority are young women).
Platform for Voice A vibrant participatory platform of youth voices shaping policy and practice.
Knowledge Generation Timely availability of relevant data and knowledge to inform policy and practice.
Advocacy Movement A movement of well-equipped young Africans advocating and seizing opportunities for dignified work.

 Uganda 2026-04-27 11:29:19 2029-04-27 Social Science and Humanities Non-Clinical Trial Non-degree Award
GODWIN ATWINE
ID: UNCST-2025-R016926
 EXTENSION SERVICES AND AGRICULTURAL PRODUCTIVITY IN UGANDA: “A STUDY OF SEMBABULE AND KALUNGU DISTRICTS”
REFNo: SS4965ES

1.To understand the level of access to agricultural extension services in Sembabule and Kalungu districts. 2.To explore farmers' perceptions on the agriculture extension services in Sembabule and Kalungu districts. 3.To examine the outcome of agricultural extension services on agricultural productivity in Sembabule and Kalungu districts.
Uganda 2026-04-27 11:18:53 2029-04-27 Social Science and Humanities Non-Clinical Trial Degree Award
Suzan Nakasendwa
ID: UNCST-2025-R021931
 Harnessing AI-driven modeling for stroke types classification and prediction of survival outcomes in Uganda and Cameroon: A retrospective observational study
REFNo: HS7282ES

1. To comprehensively assess the structure, functionality, and maturity of the stroke data ecosystem within health facilities. 2. To examine the burden of stroke in Uganda using real-world evidence data from the national DHIS2 data system 3. To develop an optimal anomaly detection model for quality assurance of routine health facility stroke data. 4. To implement multi-model stroke subtype classifiers by integrating structured data using ensemble and deep learning models. 5. To deploy statistical and machine learning models in the prediction of time to hospital discharge among stroke patients with mortality as a competing risk.
 Uganda 2026-04-27 11:16:44 2029-04-27 Medical and Health Sciences Non-Clinical Trial Degree Award
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