Study shows AI solution improves radiologist accuracy
A new Lancet study has found that the diagnostic performance of a chest x-ray AI solution, improves radiologist accuracy.
The peer-reviewed diagnostic accuracy study published in The Lancet Digital Health, on 1 July 2021, found that when used as an assist device, Annalise CXR significantly improved the ability for radiologists to perceive 102 chest X-ray (CXR) findings in a non-clinical environment, was statistically non-inferior for 19 findings and no findings showed a decrease in accuracy.
The study also assessed the standalone 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 significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings.
Annalise.ai CEO and co-founder, Dimitry Tran, said Annalise CXR would provide significant benefits to patients and healthcare professionals: “A major challenge facing global health systems is that the number of scans requiring clinical interpretation is growing at a much greater pace than increases in the number of radiologists to interpret them,” Mr Tran said.
“Annalise CXR seamlessly integrates with regular workflows, highlighting findings on chest X-rays for review by the radiologist. We hope that 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,” he commented, adding: “Annalise CXR operates in a way that more closely mimics a radiologist’s own workflow, reading an entire chest X-ray image, both frontal and laterally, and methodically searching for multiple potential findings. This enables faster reporting and reduces the likelihood of missed diagnoses, as would be the case if a narrow AI solution detected a single finding and missed other clinically relevant findings elsewhere in the scan.”
Dr. Claire Bloomfield is the CEO at the National Consortium of Intelligent Medical Imaging (NCIMI). NCIMI is a partnership between NHS Trusts, companies, universities, charities, and patient groups, coordinated by the University of Oxford, which aims is to revolutionise healthcare using AI.
Dr. Bloomfield explained: “There is real value in solutions like this that meet 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 which 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.
“The NHS has an opportunity to adopt more innovative products like Annalise CXR to keep care cutting edge, as well as support the workforce in meeting the challenges they face right now. The ecosystem of research and innovation, commercialisation and care delivery will become even more closely integrated in the coming years.”
Further details of the study results can be found here http://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00106-0/fulltext