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Google’s new deep learning system could help radiologists analyze X-rays

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Deep learning can detect abnormal chest x-rays with accuracy that matches that of professional radiologists, according to a new paper by a team of AI researchers at Google published in the peer-reviewed science journal Nature.

The deep learning system can help radiologists prioritize chest x-rays, and it can also serve as a first response tool in emergency settings where experienced radiologists are not available. The findings show that, while deep learning is not close to replacing radiologists, it can help boost their productivity at a time that the world is facing a severe shortage of medical experts.

The paper also shows how far the AI research community has come to build processes that can reduce the risks of deep learning models and create work that can be further built on in the future.

Searching for abnormal chest x-rays

The advances in AI-powered medical imaging analysis are undeniable. There are now dozens of deep learning systems for medical imaging that have received official approval from FDA and other regulatory bodies across the world.

But the problem with most of these models is that they have been trained for a very narrow task, such as finding traces of a specific disease and conditions in x-ray images. Therefore, they will only be useful in cases where the radiologist knows what to look for.

But radiologists don’t necessarily start by looking for a specific disease. And building a system that can detect every possible disease is extremely difficult “if not impossible.

“[The] wide range of possible CXR [chest x-rays] abnormalities makes it impractical to detect every possible condition by building multiple separate systems, each of which detects one or more pre-specified conditions,” Google’s AI researchers write in their paper.

Their solution was to create a deep learning system that detects whether a chest scan is normal or contains clinically actionable findings. Defining the problem domain for deep learning systems is an act of finding the balance between specificity and generalizability. On one end of the spectrum are deep learning models that can perform very narrow tasks (e.g., detecting pneumonia or fractures) at the cost of not generalizing to other tasks (e.g., detecting tuberculosis). And on the other end are systems that answer a more general question (e.g., is this x-ray scan normal or does it need further examination?) but can’t solve more specific problems.

The intuition of Google’s researchers was that abnormality detection can have a great impact on the work of radiologists, even if the trained model didn’t point out specific diseases.

“A reliable AI system for distinguishing normal CXRs from abnormal ones can contribute to prompt patient workup and management,” the researchers write.

For example, such a system can help de-prioritize or exclude cases that are normal, which can speed up the clinical process.

Although the Google researchers did not provide precise details of the model they used, the paper mentions EfficientNet, a family of convolutional neural networks (CNN) that are renowned for achieving state-of-the-art accuracy on computer vision tasks at a fraction of the computational costs of other models.

B7, the model used for the x-ray abnormality detection, is the largest of the EfficientNet family and is composed of 813 layers and 66 million parameters (though the researchers probably adjusted the architecture based on their application). Interestingly, the researchers did not use Google’s TPU processors and used 10 Tesla V100 GPUs to train the model.

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