The system proved to be just as accurate as radiologists in identifying and classifying intracranial hemorrhages from the retrospective set, and even better than non-expert human readers in its assessment of the prospective one.

In addition, the system can be deployed directly onto the scanner, allowing it to alert care teams to the presence of a hemorrhage for appropriate further testing to take place before the patient is even off the scanner.


“The next step will be to deploy the system into clinical areas and further validate its performance with many more cases,” said author Shahein Tajmir, a radiology resident at MGH Radiology. “We are currently building a platform to allow for the widespread application of such tools throughout the department. Once we have this running in the clinical setting, we can evaluate its impact on turnaround time, clinical accuracy and the time to diagnosis."

Each of the 904 heat CT scans used to train the system consisted of around 40 individual images that were labeled by five MGH neuroradiologists.

Partial support was provided through a grant by the National Institutes of Health.

The findings were published online in the journal, Nature Biomedical Engineering.

Back to HCB News

Cardiology Homepage