A recent study published in Radiology revealed that the average delay in the processing of abnormal chest X-rays can be a reduced from 11 to just three days through the use of AI.
Researchers from the University of Warwick fed more than 470,000 anonymized adult chest radiographs to train the AI using a natural language program. The AI-drive neural network was then able to predict the clinical priority of the radiographs in real time.
Samara Rosenfeld explains further in Healthcare Analytics News:
The NLP system had a sensitivity of 96 percent, specificity of 97 percent, positive predictive value of 84 percent and negative predictive value of 99 percent for critical findings. For normal radiographs, the system achieved a sensitivity of 98 percent, specificity of 99 percent, positive predictive value of 97 percent and negative predictive value of 99 percent.
The AI significantly reduced the average delays for the examinations reported as critical from between 11 to 18 days to about 3 to 12 days. The average delay was reduced from just over seven hours to 43 minutes.
With the system implemented, 85 percent of the examinations labeled as critical would have been reported within the first day, compared to 60 percent reported from the historical data.
Overall, the AI-based system detected abnormal from normal adult chest X-rays with a high positive predictive value of 94 percent, according to the study.
“Artificial intelligence-led reporting of imaging could be a valuable tool to improve department workflow and workforce efficiency,” said study lead Giovanni Montana, professor and chair in data science in the Warwick Manufacturing Group at the University of Warwick.
