Can Artificial Intelligence Spot Early Warning Signs of Oral Cancer?

Can artificial intelligence spot early warning signs of oral cancer? Kingston University experts to develop system for mobile phone app in new study.

Kingston University experts are exploring how artificial intelligence could be trained to detect the early signs of oral cancer using a mobile phone app.
Professors Sarah Barman and Paolo Remagnino have secured £146,000 for the two year Medical Research Council-funded project, which will see them work alongside experts from the University of Malaya and Cancer Research Malaysia.
Oral cancer is the eighth most common cancer worldwide, with 300,000 cases diagnosed each year. As with many cancers, catching the disease at an early stage is crucial. The project will see the Kingston University and University of Malaya teams train an artificial intelligence system to spot early warning signs using images taken of the inside of the mouth – referred to by medics as the oral cavity – on mobile phones.
They will use a technology called deep learning, a form of machine learning which mimics the way the brain makes connections between pieces of information without being specifically programmed through artificial neural networks. If successful, it could help save both time and money on triaging patients, with potentially far-reaching benefits for the NHS in the long term.
“We’re basically training a system to detect abnormalities in the mouth that could be the early indications of oral cancer,” Professor Barman said. “Our challenge is to develop deep learning models that demonstrate a high accuracy and prediction of disease.
“If we find this approach is reliable enough, artificial intelligence could be used for other forms of disease screening with a wide range of possible applications in the field of medical diagnostics. The idea that you could take an image on a mobile phone, then use artificial intelligence to quickly determine whether that patient needs referring or not, is really exciting.”

“Our challenge is to develop deep learning models that demonstrate a high accuracy and prediction of disease.
“If we find this approach is reliable enough, artificial intelligence could be used for other forms of disease screening with a wide range of possible applications in the field of medical diagnostics. The idea that you could take an image on a mobile phone, then use artificial intelligence to quickly determine whether that patient needs referring or not, is really exciting.”

Ensuring early diagnosis is possible is a particular challenge in rural Asia, including parts of Malaysia, due to a lack of easy access to healthcare and specialist treatment. To combat this, Cancer Research Malaysia has developed a phone app called MeMoSA (Mobile Mouth Screening Anywhere). The app can capture images of the oral cavity that can be interpreted remotely, but is still reliant on the availability of oral medicine and surgical specialists to view the images and make a decision on which are referable, which can be expensive and time consuming. The hope is that artificial intelligence could speed up this process.
“With the possibility of increasing survival rates for oral cancer patients, the incorporation of artificial intelligence within MeMoSA holds a lot of promise in ensuring that the efforts we make in early detection continue to break barriers across regions, particularly in south and south-east Asia where the disease is most prevalent,” Professor Dr Sok Ching Cheong, of Cancer Research Malaysia, said.

The Kingston University team will be training the system with thousands of images of normal mouths and examples of different categories of abnormality provided by experts from the University of Malaya and Cancer Research Malaysia. They will then examine the accuracy of the system’s identification of referable and non-referable cases to see how closely it matches an expert clinician’s opinion.
“In Malaysia they have already done studies with their app that shows you can take a photo of your mouth and send to an expert to judge if it’s likely to be malignant, or referable,” Professor Remagnino said. “However, there is always a time delay when sending these images to a specialist remotely – as well as a reliance on their availability – which is where a system like this that can automatically triage patients could make a real difference.”
As part of the project, the team will also be using a further type of artificial intelligence called a generative adversarial network, or GAN, which can create realistic images based on what it has learned – from images of people or animals through to generating original works of art.
“We will be looking to see whether we can use the GAN to create unique images of mouths, both normal and with abnormalities, to help provide additional data for training the artificial intelligence system,” Professor Remagnino added.

Funding for the project was allocated through the Medical Research Council’s Cancer Research and Global Health Initiative call, part of the Global Challenges Research Fund. The fund was launched by the government in 2015 to ensure UK research takes the lead in addressing some of the diverse challenges faced by developing countries.

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