AI System Detects Key Findings in Chest X-rays of Pneumonia Patients

Researchers from Intermountain Healthcare (Salt Lake City, UT, USA) and Stanford University (Stanford, CA, USA) have demonstrated that it takes just 10 seconds for an automated artificial intelligence- (AI) based chest X-ray interpretation model to accurately identify key findings in chest X-rays of patients suspected of having pneumonia.

The researchers studied the CheXpert system, an automated chest X-ray interpretation model developed at Stanford University that utilizes AI to review X-ray images taken at several emergency departments. The CheXpert model was developed by the Stanford Machine Learning Group, which used 188,000 chest imaging studies to create a model that can determine what is and is not pneumonia on an X-ray.

The researchers found that the CheXpert system accurately identified key findings in X-rays - with high agreement to a consensus of three radiologists - in about 10 seconds, thus significantly outperforming current clinical practice. The study found that those ultra-quick findings could enable physicians reading X-rays to accurately confirm a pneumonia diagnosis significantly faster than current clinical practice, enabling treatment to start sooner, which is vital for severely ill patients suffering from pneumonia.

"CheXpert is going to be faster and as accurate as radiologists viewing the studies. It's an exciting new way of thinking about diagnosing and treating patients to provide the very best care possible," said Nathan C. Dean, MD, principal investigator of the study, and section chief of pulmonary and critical care medicine at Intermountain Medical Center in Salt Lake City.

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Intermountain Healthcare
Stanford University