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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.
“Five years prior to ever having a symptom is when you might have to detect ovarian cancer, to affect mortality,” says Ms Moran.
But new blood tests are emerging that use the power of artificial intelligence (AI) to spot signs of the cancer in its very early stages.
And it’s not just cancer, AI can also speed up other blood tests for potentially deadly infections like pneumonia.
Memorial Sloan Kettering Cancer Center
Dr Daniel Heller is a biomedical engineer at Memorial Sloan Kettering Cancer Center in New York.
His team has developed a testing technology that uses nanotubes – tiny tubes of carbon that are around 50,000 times smaller than the diameter of a human hair.
About 20 years ago, scientists began discovering nanotubes that can emit fluorescent light.
In the past decade, researchers learned how to change these nanotubes’ properties so they respond to almost anything in the blood.
Now it is possible to put millions of nanotubes into a blood sample and have them emit different wavelengths of light based on what sticks to them.
But that still left the question of interpreting the signal, which Dr Heller likens to finding a match for a fingerprint.
In this case, the fingerprint is a pattern of molecules binding to sensors, with different sensitivities and binding strengths.
But the patterns are too subtle for a human to pick out.
“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.
These included blood from people with other forms of cancer or other gynaecological diseases that might be confused with ovarian cancer.