AI saves radiologists time by triaging chest x-rays, but is it clinically feasible?
There’s been plenty of hype around the potential of artificial intelligence (AI) to ease radiology workloads. And a new convolutional neural network approach detailed in a Jan. 22 Radiology study greatly reduced reporting backlog by accurately triaging chest x-rays in real-time.
"The initial results reported here are exciting as they demonstrate that an AI system can be successfully trained using a very large database of routinely acquired radiologic data," said co-author Giovanni Montana, PhD, of the University of Warwick in Coventry, England, in a prepared statement. "With further clinical validation, this technology is expected to reduce a radiologist's workload by a significant amount by detecting all the normal exams so more time can be spent on those requiring more attention."
The team used more than 470,000 de-identified adult chest x-rays to develop their AI platform. A natural language processing (NLP) algorithm analyzed the radiology reports to mark the image as either critical, non-urgent or normal—a “critical milestone” in the study, Montana noted.
After testing the system on an independent set of 15,887 images, they reported the platform detected normal chest x-rays with a sensitivity of 71 percent, specificity of 95 percent, positive predictive value of 73 percent and negative predictive value of 99 percent.
After simulations, Montana and colleagues reported critical findings received a radiologist opinion in 2.7 days, on average, much quicker than the 11.2 day average for a real-world practice. For urgent findings, average reporting was reduced to 4.1 days, down from 7.6 days.
There were a number of limitations cited by the study authors. In a related editorial, William F. Auffermann, MD, PhD, with University of Utah Health in Salt Lake City, took issue with the team’s use of data from a single health system. Auffermann argued that an algorithm trained with data from one institution may not translate well in other centers.
A study published Nov. 6, 2018 in PLOS Medicine tested this notion, and found algorithms trained to detect pneumonia on chest x-rays performed poorly when tested on data from another health system.
Auffermann did note the system described in the study could have a “substantial” impact on decreasing turnaround times if used in a clinical setting, but emphasized more testing is needed before that becomes a reality.
“One may imagine a future AI triage system where the patient is held briefly in the radiology department while an AI algorithm performs a rapid initial triage for critical findings before the patient leaves the department,” Auffermann concluded. “The exact manner in which such a system may be implemented will require further study before it is ready to assist in patient care.”
Matt joined Chicago’s TriMed team in 2018 covering all areas of health imaging after two years reporting on the hospital field. He holds a bachelor’s in English from UIC, and enjoys a good cup of coffee and an interesting documentary.