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This is the third feature in a six-part series that is looking at how AI is changing medical research and treatments.
Ovarian cancer is “rare, underfunded, and deadly”, says Audra Moran, head of the Ovarian Cancer Research Alliance (Ocra), a global charity based in New York.
Like all cancers, the earlier it is detected the better.
Most ovarian cancer starts in the fallopian tubes, so by the time it gets to the ovaries, it may have already spread elsewhere too.
“We can look at the data and we will not make sense of it at all,” he says. “We can only see the patterns that are different with AI.”
Decoding the nanotube data meant loading the data into a machine-learning algorithm, and telling the algorithm which samples came from patients with ovarian cancer, and which from people without it.
Dr Heller has high hopes for the tech.
“What we’d like to do is triage all gynaecological disease – so when someone comes in with a complaint, can we give doctors a tool that quickly tells them it’s more likely to be a cancer or not, or this cancer than that.”
A big challenge in using AI to develop blood tests for ovarian cancer research is that it is relatively rare, which limits the data for training algorithms.
It’s not just early detection that AI is potentially useful for, it is also speeding up other blood tests.
For a cancer patient, catching pneumonia can be deadly and, as there are around 600 different organisms that can cause pneumonia, doctors have to conduct multiple tests to identify the infection.
Karius has a database of microbial DNA which has tens of billions of data points.
It’s not just a question of identifying the infection, but also selecting the right antibiotic for it.
Over the last two years Dr Slavé Petrovski has developed an AI platform called Milton that, using biomarkers in the UK biobank data to identify 120 diseases with a success rate of over 90%.
These are often complex patterns, where there may not be one biomarker, but you have to take into consideration the whole pattern.
“We know that the sensor binds and responds to proteins and small molecules in the blood, but we don’t know which of the proteins or molecules are specific to cancer,” he says.
More broadly data, or the lack of it, is still a drawback.
“More data can improve the algorithm, just as algorithms for self-driving cars can improve with more testing on the street.”
One challenge is that researchers don’t necessarily currently understand all the connections that an AI might make between the test biomarkers and the diseases.
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