2026 Projects
2026 Project: Africa
AI-assist for MRI safety and image quality: A study to develop AI tools to enhance patient safety, workflow compliance, and captured image quality for MRI in resource-limited settings
Project Team
- Abdulrazaq Zubair (Lead), Federal University of Health Sciences, Azare, Medserve Kano Diagnostic Center/AKTH,
- Nafiu Musa Muhammad, B.Rad Medserve Kano/Diagnostic center/AKTH
- Mubaraq Yakubu, MSc King’s College London/AKTH
- Abbas Rabiu Muhammad, MBBS, MSc Bayero University/Aminu Kano Teaching Hospital
- Mohammed Abba, PhD Bayero University/Aminu Kano Teaching Hospital
- Abba Muhammad, Baze University Abuja, Nigeria
- Zulyadaini Muhammad Aminu, Omdena Kano State AI for Smart Farming Project
- Yunusa Mohammed Garba, PhD Gombe State University, Nigeria
- Charles Delahunt, PhD University of Washington, USA
- Tarisiro Matiza, Mphil Market Access Africa
Location
Nigeria
Executive Summary
Magnetic Resonance Imaging (MRI) is indispensable in modern diagnostics, and expanding the effective use of MRI in Africa will yield both improvements to individual care and the means to create Africa-centric MRI datasets necessary to develop AI-assisted diagnostics suitable for African populations. But patient-safety incidents and poor image quality remain persistent challenges in much of Africa. Many MRI centres operate under heavy workloads, with limited staff training, outdated systems, and paper-based screening forms, leading to incomplete safety checks, inconsistent image quality, and a lack of structured patient metadata. These gaps hinder both patient care and the creation of usable Africa-centric MRI datasets.
Therefore, this project proposes an AI-assisted decision-support system at the pre-acquisition stage, to enhance MRI safety compliance, image quality, and complete metadata collection in resource-limited settings through three integrated modules:
- Automated Pre-MRI Safety Screening, which standardizes safety protocols and assists the MR radiographers/technologists in verifying implant safety and contraindications.
- Image Quality Optimization, a recommendation engine suggesting patient-specific acquisition parameters to minimize artefacts and repeat scans; and
- Digital Data Recording, which replaces paper forms with a structured electronic system for reliable documentation and metadata capture. A pilot study currently underway in Nigerian imaging centres, assessing MRI safety knowledge and compliance, has revealed wide variations in practice and reliance on paper-based methods, underscoring the need for standardized, data-driven safety procedures.
Building on this preliminary evidence, this project aims to develop and test AI tools to improve safety-screening compliance, reduce repeat scans, enhance captured image quality, and ensure complete patient metadata collection. The project is clinician-led, and includes both clinical and AI teams, to ensure that the AI development is informed by and serves clinical needs. The project will establish the first structured MRI safety database in Nigeria, and will develop AI tools to advance the ability of hospitals and clinics in low-resource countries to generate MRI datasets of high quality for future use in AI research.