Johan Lundin, Karolinska Institutet

DEVELOPING WIDELY AVAILABLE DIAGNOSTICS

Johan Lundin is working to enable the use of AIsupported image diagnostics even in resource-limited areas. A mobile digital microscope that can be used in the field has proven to enable diagnosis of both cancer and infectious diseases.

Analysing microscopic samples of cells and other tissue is essential for being able to make a correct diagnosis. In cancer prevention, for example, pathologists assess whether a particular cell change is a preliminary stage of cancer. Johan Lundin, a professor at the Department of Global Public Health at Karolinska Institutet in Solna, has ongoing partnerships in Tanzania and Kenya where there is a shortage of pathologists.

“In sub-Saharan Africa there is estimated to be on average fewer than one pathologist per million people. Compare that to Sweden, where there is considered to be a shortage with 30 pathologists per million people,” says Johan Lundin.

Using digital technology and artificial intelligence (AI) he and his colleagues want to spread access to image-based diagnostics. AI technology based on ‘deep learning’ has revolutionised image-based pattern recognition. Today this is available in everything from mobile phones with facial recognition to self-driving cars, and to an ever-increasing extent also within medical diagnostics. It means that the system can learn to recognise structures that distinguish cancer cells or can find malaria parasites in a blood sample.

Initially the researchers developed a prototype digital microscope that they call MoMic using components from the mobile industry. Using this instrument and similar mobile microscopes that are now also commercially available, samples of blood or cells can be analysed and data forwarded from the device.

“So the person assessing the images doesn’t actually need to be in the same room – the images can just as easily be sent to another city or another country,” he says.

Having an expert go through digital samples that are then used to train the AI enables the AI to learn. “People get tired, but the AI goes through every sample with equal speed and care. The system is also consistent and assesses the same sample exactly the same on different occasions,” says Johan Lundin.

He highlights the fact that the project involves representatives of Karolinska Institutet, Uppsala University and the University of Helsinki.

“Those of us in the Nordic management team work closely with each other as well as with our team members on the spot in Kenya and Tanzania. These local workers have learnt to use the method and are in turn teaching it to others in their home regions,” says Johan Lundin.

In the project ‘Artificial intelligence for diagnostics of cancer and infectious disease in resource-limited settings – the MoMic Project’ they will now apply the method in three different areas. In one subproject they will see whether the method can be used to screen for cervical cancer, which is the most common cause of cancer mortality in women in sub-Saharan Africa.

“One factor is that women with HIV have an increased risk of also carrying the human papilloma virus, which causes cervical cancer. Since few have access to screening, the risk of the cancer managing to spread before it is discovered increases,” says Johan Lundin.

The researchers are working with a hospital in Kinondo in rural Kenya, where women with HIV go to get their medicines every three months.

“We were able to take cell samples in conjunction with these check-ups and those who had changes received treatment,” says Johan Lundin.

Last year they showed in a study involving 740 women that AI had an accuracy of between 96 and

Johan Lundin and his colleagues Andreas Mårtensson and Nina Linder want to spread access to image-based diagnostics to places where there are no pathologists.

Analyst Felix Kinyua stains cell samples in the lab in Kenya.

100 percent when it came to finding changes in cell samples, as they published in JAMA Network Open. However, AI was somewhat worse at interpreting samples from low-grade cancer, which it is hoped can be improved in the follow-up now planned that will involve 1, 500 women from various clinics in the area.

“We will also be following up the thirty or so women from the first study who had cell changes, to ensure that the local freezing treatment they received was sufficient to stop the cancer developing,” he says.

Malaria affects 200 million people annually, the majority of them children under the age of five. In a study published in PLOS One in 2020 the researchers showed that their method could be used to find malaria parasites in microscopy images of blood smears. A staining method is used that causes the parasites to ‘light up’, which means the magnification does not need to be extreme. Since resistance to drugs is becoming increasingly common, in a new study they will analyse blood samples from 800 patients that are taken before treatment and then again three days after, to assess how the treatment works.

The third sub-project is to investigate the incidence of infection with parasitic worms which nearly 30 percent of children in Tanzania and Kenya carry. “The children rarely become seriously ill, but can suffer from fatigue, malnutrition and poor growth and don’t have the energy to keep up in school as well as others. Today it’s common for all pupils in the school to be given a worming treatment, regardless of whether or not they are infected,” says Johan Lundin.

He thinks that with more efficient and simplified diagnostics the treatment could be given only to those children who are infected – thereby also reducing the risk of drug resistance.

The researchers will study how good AI is at finding worm eggs in faecal samples compared with a skilled human microscopist. They described this method in a study from 2017 in Global Health Action. In an as yet unpublished study Johan Lundin says they saw that AI found the around 10 percent of samples containing worm eggs that a human misses.

“It’s no wonder that they do, since an entire microscope slide may have only one or two eggs – so it really is like looking for a needle in a haystack. But the AI is meticulous and fast, and searches through a whole sample in a tenth of the time it takes a human.”

The aim of the new study is to see whether this method can be used to rapidly distinguish whether or not a child is infected, to avoid unnecessary medication.

>96%

The accuracy of the researchers’ AI at finding cell changes in microscopy images that could result in cervical cancer.

1

The AI takes one minute to investigate microscopy images of faecal samples for eggs from parasitic worms. It takes a skilled microscopist 10 minutes.

200

200 million people are affected by malaria annually around the world.

ABOUT THE PROJECT Project manager: Johan Lundin, a professor in the Department of Global Public Health at Karolinska Institutet in Solna and also Research Director at the Institute for Molecular Medicine in Finland (FIMM), University of Helsinki.

Title: ‘Artificial intelligence for diagnostics of cancer and infectious disease in resource-limited settings – the MoMic Project’.

What it involves: Using a microscope connected to the mobile network, image data can be analysed using artificial intelligence (AI) or a combination of AI and medical expertise. This opens the way for better image diagnostics even in resource-limited areas.

Who: Researchers from Karolinska Institutet, Uppsala University and the University of Helsinki, and from Kinondo Hospital in Kenya and Muhimbili University of Health and Allied Sciences in Tanzania.

Funding: The Erling-Persson Foundation is supporting the project with a total of SEK 10 million over three years.