Page 66 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

sufficient training data, variability in image quality and environmental factors may still pose
challenges.

In conclusion, the smartphone-over-microscope approach combines modern technology with
community health efforts to improve malaria detection significantly, ultimately aiming for better
health outcomes in underserved populations.

2�2 Benefits of the use case

By leveraging AI technology, this approach enhances diagnostic accuracy and accessibility,
making it a vital tool in combating malaria, a leading cause of morbidity and mortality in many
developing regions.

AI-driven image analysis automates the detection of malaria parasites, ensuring timely diagnoses
that can significantly improve patient outcomes and reduce the burden on healthcare systems.
This helps to expand access to essential health services and promoting universal health
coverage, particularly in underserved areas. Empowering community health workers with AI
tools eliminates barriers caused by shortages of trained medical professionals and offers a
scalable solution to monitoring and managing malaria outbreaks.

Furthermore, the technological innovation represented by this use case fosters resilient
infrastructure and promots research and technological development. As AI models are trained
on datasets like the Lacuna Malaria Detection Challenge, they can evolve to support various
health initiatives beyond malaria, thus enhancing local health capacities.

In summary, the integration of AI in malaria detection not only addresses immediate health
challenges but also contributes to broader efforts in innovation, infrastructure development,
and improving health equity.

Partners
Bolgatanga Technical University: Responsible for collecting additional blood slide samples
from local healthcare facilities to expand and diversify the dataset. Their role ensures the AI
model is trained on region-specific data, improving its accuracy and robustness for real-world
deployment in rural communities.

2�3 Future Work

Future work for this project includes expanding the blood slide dataset by collaborating with
more local clinics through Bolgatanga Technical University, ensuring the model has a diverse
and representative set of images for better generalization. The AI model will be further improved
by incorporating image augmentation techniques to address issues related to varying lighting
and image quality conditions. Additionally, a user-friendly mobile application interface will be
developed to make the smartphone-microscope setup easier to operate by community health
workers with minimal training.

To support these enhancements, upgraded smartphones with better camera resolution and
processing power will be required to handle more complex TinyML models. More microscopes
with adaptable mounts for stable smartphone imaging will also be needed, along with funding

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