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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 t issue is essential for being able to make a correct d iagnosis. In cancer prevention, for example, pathologists assess whether a particular cell change is a preliminary stage of cancer. Johan Lundin, a professor at the D epartment 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.
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“People get tired, but the AI goes through every s ample with equal speed and care. The system is also c onsistent 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 d iagnostics 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
