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code for a cure

Using AI to treat river blindness

For our annual Global Data Science Challenge, nearly 900 participants across 31 countries helped a university hospital in Germany harness the power of artificial intelligence and machine learning to improve diagnosis and develop new treatments for river blindness.

Twenty million people around the world suffer from a tropical disease known as river blindness, or onchocerciasis.
Caused by parasitic worms transmitted through fly bites, it is most commonly found in sub-Saharan Africa. Categorized as a neglected tropical disease (NTD), it can lead to permanent blindness if not treated effectively.

Developing new treatments for the disease requires a time-consuming process of tissue sample analysis, and there is a shortage of medical professionals who have the necessary expertise.
This was the brief given to Capgemini colleagues around the world as part of this year’s Global Data Science Challenge (GDSC) for a sustainable future.

Empowering our talent to make a difference with AI

The 5th GDSC was launched in collaboration with the Institute of Medical Microbiology, Immunology and Parasitology at the University Hospital Bonn, in support of a global effort to eliminate river blindness in 10 countries by 2030.

Previous editions of GDSC have also aimed to create a sustainable future – teams have worked on tracking sperm whales with AI and helping a Norwegian marine observatory identify ocean anomalies.

Our new goal was to create an AI-based solution, using the Amazon Web Services (AWS) platform, that could scan images of tissue samples to identify the presence of parasitic worms.

an expert examines tissue samples used to identify the presence of parasitic worms that cause river blindness. (source supplied)

The solution would need to demonstrate the ability of AI to effectively diagnose the stage of worm development within the patient, as well as machine learning’s capacity to speed up the review process needed for new treatments.

Working closely together to win the challenge

The competition was won by the Insights & Data team in India: Utkarsh Prakash, Abhijeet Gorai, Prince Raj, and Deepak Pandey, who are all data scientists. In what was a hard-fought contest, their solution showed the most improvement in tissue sample review accuracy.

The colleagues knew each other well – Utkarsh, Abhijeet, Prince, and Deepak all joined Capgemini in 2019 and were in the same data science training group, and Abhijeet and Deepak even graduated from the same university.  

“This was the second time we participated in the GDSC as a team,” says Utkarsh. “Based on our experience and our knowledge of each other’s skills, we think we make a good team.”

Getting their heads together

“We dedicated an hour at the end of each working day to work on the project, for about two months,” explains Prince. “We would get together online to share our new ideas and explore solutions.”

The team was driven to enter the competition by the promise of learning new skills, says Abhijeet. “We are all quite early on in our careers, and we knew this competition would expose us to new technologies and ways of working, particularly object recognition.”

Utkarsh adds that medical AI is a particularly fertile sector. “This area is booming right now. We knew if we could learn more about this area, it would help us in our careers, enable better solutions for clients, and, of course, contribute to making the world a healthier place.”

Tissue sample images like this one are used to train the AI model (source supplied)

Sharing ideas across the globe

As part of the challenge, an online working environment enabled current and past participants to receive communications and updates and share best practice solutions, all across the world.

“Although the teams were competing against one another, in the earlier rounds we were all sharing information regarding how to overcome certain challenges,” says Deepak. “This ensured every team was working with the best available solutions – which raised the overall standard of the entries.”

A bright future for AI

University Hospital Bonn intends to develop their winning solution and cherry-pick the best ideas from all the entrants. Moreover, according to Utkarsh, the prospects for AI solutions within the wider medical sector are very promising. “There’s an abundance of data just sitting there, waiting to be used,” he says. “We’ve learned first-hand how it’s possible to create efficient automation systems with such data, to save time and allow researchers to focus on the bigger issues.”

a researcher examines tissue samples under a microscope (source supplied)

Prince explains that the model they have created could also have wider applications.

“Our learning model will work for any dataset, for any object recognition requirement in the medical sector – for example, identifying cancerous cells,” he says. “It would even work in, say, an airport terminal baggage-handling system, where object detection is required.”

Seeing the results

An exciting part of winning GDSC is to receive an award that enables the team to see their winning solution come to life. In addition to claiming a prize of a trip to the University Hospital Bonn to see the work of medical professionals fighting the disease, the team will get their AWS certification exam free of charge as an added bonus. For Utkarsh and his colleagues, the whole experience has been extremely worthwhile. “The competition is a fantastic platform for learning – we couldn’t recommend it enough. We’re so proud to be making a difference, helping doctors to make the world a healthier place.”

Data and Artificial Intelligence

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