A chest x-ray AI decision support tool for radiologists and clinicians that detects 124 clinical findings has been launched. The launch of Annalise CXR by annalise.ai coincides with the publication of a peer-reviewed diagnostic accuracy study published by The Lancet Digital Health, which is said to be the largest of its kind undertaken in terms of the number of findings concurrently evaluated.
The study found that when used as an assist device, Annalise CXR significantly improved the ability of radiologists to perceive 102 chest x-ray findings in a non-clinical environment, was statistically non-inferior for 19 findings and no findings showed a decrease in accuracy.
It also assessed the stand-alone performance of the model in a non-clinical environment against radiologists in identifying chest x-ray pathology, as well as investigating the effect of model output on radiologist performance when used as an assist device.
Annalise CXR’s AI model classification alone was said to be significantly more accurate than unassisted radiologists for 117 of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings.
Co-founder and ceo Dimitry Tran said: “A major challenge facing global health systems is that the number of scans requiring clinical interpretation is growing at a greater pace than increases in the number of radiologists to interpret them. Annalise CXR integrates with regular workflows, highlighting findings on chest x-rays for review by the radiologist.
“We hope the solution will increase radiology capacity, thereby reducing turnaround time, improving interpretation quality by providing clinicians with another set of eyes and reducing the risk of backlogs.”
The 124 clinical findings detected by Annalise CXR compares with 75 findings found using the next most comprehensive CXR AI product, with most CXR AI products limited to fewer than 15 findings, the company reports.
Dr Claire Bloomfield is ceo at the National Consortium of Intelligent Medical Imaging, a partnership between NHS trusts, companies, universities, charities and patient groups coordinated by the University of Oxford that aims to revolutionise healthcare using AI. Dr Bloomfield said: “There is real value in solutions like this that meet the specific needs of healthcare professionals by reducing repetitive tasks and improving workflow. AI solutions have the potential to free up time for overworked and under-resourced radiologists, so they can focus on patient-facing decisions and cases that call on their years of experience. There is also scope for radiologists to increasingly use ‘multi-modal’ data as part of decision making, placing them at the heart of diagnosis of patients beyond clinical images.”
annalise.ai was originally formed as a joint venture between Australian healthcare technology company Harrison.ai and one of the world’s largest radiology companies I–MED Radiology Network.
Picture: Annalise CXR helps reduce repetitive tasks to improve workflow.
Published on page 14 of the August 2021 issue of RAD Magazine.