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Could artificial intelligence be the future of cancer diagnosis?

In a recent study, researchers trained an algorithm to differentiate between malignant and benign lesions in scans of breast tissue.

Doctor and patient with breast scanA new study asks whether artificial intelligence could streamline cancer diagnosis.


With cancer , the key to successful treatment is catching it early.

Researchers are keen to improve the diagnostic process to avoid these issues. Detecting whether a lesion is malignant or benign more reliably and without the need for a biopsy would be a game changer.

Some scientists are investigating the potential of artificial intelligence (AI). In a recent study, scientists trained an algorithm with encouraging results.

AI and elastography

Ultrasound elastography is a relatively new diagnostic technique that tests the stiffness of breast tissue. It achieves this by vibrating the tissue, which creates a wave. This wave causes distortion in the ultrasound scan, highlighting areas of the breast where properties differ from the surrounding tissue.

From this information, it is possible for a doctor to determine whether a lesion is cancerous or benign.

Although this method has great potential, analyzing the results of elastography is time-consuming, involves several steps, and requires solving complex problems.

Recently, a group of researchers from the Viterbi School of Engineering at the University of Southern California in Los Angeles asked whether an algorithm could reduce the steps needed to draw information from these images. They published their results in the journal Computer Methods in Applied Mechanics and Engineering.

The researchers wanted to see whether they could train an algorithm to differentiate between malignant and benign lesions in breast scans. Interestingly, they attempted to achieve this by training the algorithm using synthetic data rather than genuine scans.

Synthetic data

When asked why the team used synthetic data, lead author Prof. Assad Oberai says that it comes down to the availability of real-world data. He explains that “in the case of medical imaging, you’re lucky if you have 1,000 images. In situations like this, where data is scarce, these kinds of techniques become important.”

The researchers trained their machine learning algorithm, which they refer to as a deep convolutional neural network, using more than 12,000 synthetic images.

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