Skip to main content
SPARK Academy

Sprint AI Training for African Medical Imaging Knowledge Translation

From science
to practice

Building Africa's next generation of medical AI experts.

We train a new generation of African AI experts in medical imaging who can train others, turning AI knowledge into action through case-based, interdisciplinary learning.

Welcome

Welcome to SPARK Academy

Designed for sustainability

SPARK builds skills step by step, from a foundational course to a hackathon, a practicum and SPARK Next, so that graduates go on to train others and the knowledge stays local.

Read more
The SPARK approach, from trainees to trainers: Teach, the foundational course: fundamentals of AI and medical imaging. Try, the hybrid hackathon: code hack exercises and interdisciplinary seminars. Use, the practicum: build state-of-the-art AI models, scientific communication, and taking part in a grand challenge. Go, SPARK Next: need assessment, identifying knowledge gaps, and applying acquired skills to local problems.

Plays from YouTube. Nothing loads from YouTube until you press play; the picture above is a copy kept on this site.

Capacity building

Centred on capacity building and solving local problems

The need

The need for skilled AI training

25–40%

of the global disease burden falls on Africa and other low- and middle-income countries (LMICs), which have the fewest medical imaging resources. There is a critical gap in the interdisciplinary AI skills needed to turn imaging data into clinical tools.

Current training often lacks local context. SPARK closes this gap by focusing on medical imaging processing and AI programming for local clinical needs.

World map. Countries highlighted in orange are labelled LMICs (low- and middle-income countries), including most of Africa, Asia and Latin America.
LMICs

90% of sub-Saharan Africa (SSA) children die from cancer while 85% survive in high-income countries (HICs).

44–50%

of the SSA population is under 18 years old.

1 in 3 children born today are in Africa.

The challenge

Barriers to closing healthcare gaps

Four barriers surround one central problem: few to no Picture Archiving and Communication Systems (PACS).

INFRASTRUCTURE COMPUTATIONAL RESOURCES DATASETS PERSONNEL Few to no Picture Archiving and Communication System (PACS)
  • Infrastructure

    Poor access to imaging technologies (density of MRI, CT, radiology, x-ray and ultrasound in Africa compared to high-income countries).

  • Computational resources

    Low capacity and few resources, including servers and high-speed network connectivity.

  • Personnel

    Availability of skilled paediatric radiologists, subspecialty training, and awareness of AI and AI competence.

  • Datasets

    Few open, annotated datasets.

Our approach

Train the trainer strategy

SPARK uses a case-based curriculum and a train-the-trainer model to give free access to hands-on skill development, so that knowledge stays local and keeps spreading.

See the programme
  • Train-the-trainer

    Top graduates return as team leads and mentors for the next cohort.

  • Free to attend

    SPARK is a free, hands-on, case-based training programme.

  • Real African imaging data

    Teams apply what they learn to local problems, using real African imaging data.

  • A growing network

    Trainees, mentors and graduates stay connected, and graduates come back as mentors for new cohorts.

In their own words

What our community says

SPARK in numbers

A growing pan-African network

Partners and funders

Built with a global community

Our footprint

Where SPARK participants come from

The map fills in as each cohort's records are added.

Ready to build the future of AI in medical imaging?

Learn it, build it, and teach it, with clinicians and scientists across Africa.

Apply to SPARK