AsianScientist (Dec. 20, 2018) – A team of researchers in Japan have devised an artificial intelligence (AI) system that can identify different types of cancer cells using microscopy images. Their method can also be used to determine whether the cancer cells are sensitive to radiotherapy. The researchers reported their findings in the journal Cancer Research.
In cancer patients, there can be tremendous variation in the types of cancer cells in a single tumor. Identifying the specific cell types present in tumors can be very useful when choosing the most effective treatment. However, making accurate assessments of cell types is time consuming and often hampered by human error and the limits of human sight.
To overcome these challenges, scientists led by Professor Hideshi Ishii of Osaka University, Japan, have developed an AI system that can identify different types of cancer cells from microscopy images, achieving higher accuracy than human judgement. The system is based on a convolutional neural network, a form of AI modeled on the human visual system.
“We first trained our system on 8,000 images of cells obtained from a phase-contrast microscope,” said corresponding author Ishii. “We then tested [the AI system’s] accuracy on another 2,000 images and showed that it had learned the features that distinguish mouse cancer cells from human ones, and radioresistant cancer cells from radiosensitive ones.”
The researchers noted that the automation and high accuracy of their system could be very useful for determining exactly which cells are present in a tumor or circulating in the body. Knowing whether or not radioresistant cells are present is vital when deciding whether radiotherapy would be effective. Furthermore, the same procedure can be applied post-treatment to assess patient outcomes.
In the future, the team hopes to train the system on more cancer cell types, with the eventual goal of establishing a universal system that can automatically identify and distinguish all variants of cancer cells.
Source: Osaka University; Photo: Shutterstock.
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