Efficiency Gains from a Digital Pathology Workflow
I am John Tomaszewski. I am the Chair of Pathology and Anatomical Sciences at the University of Buffalo in the SUNY system in Buffalo, New York.
There are many opportunities for efficiency and they go through the entire diagnostic cycle. So, we can begin at the point of a pathologist actually assembling a case. Digital pathology allows you an automated or assistive way of pulling together all the cases that are assigned to you. And, not only the cases, but retrieving the information that you need to intelligently and effectively interpret the case, so it’s sort of a go-fetch type of mechanism.
Beyond that, the actual physicality of a pathologist mounting a glass slide to microscope and reviewing it is work, and that work can be made more efficient by the computational presentation of the images to the pathologist.
Once you have a hue of digital images, you can actually put them in a hierarchy. So you can put the most difficult cases first if, as in a digital pathology environment, you would actually have some logic in there that would have done a prescreen of your cases and help you put the most problematically difficult cases first, earlier in the day when you are fresher and can put more thought power into your diagnostics.
So assembling of the cases; presentation of the cases are two major areas that I think will be early in the efficiency gains of digital pathology. But, probably at the heart of the whole thing, is to be able to do things that you just can’t do any other way now. And that would be the value-added computational analyses of images and slides that you can’t do today. And so these are algorithms that compute on the slides that you’re looking at …the digital slides that you’re looking at… and allow you to then create new information that you just can’t get any other way. And so I think that’s sort of at the heart of it; the sort of algorithmic image analysis that could be embedded in the digital pathology environment.
Once you get your data, you can in a digital pathology environment, create computational diagnostic support systems, so called CDS’s. And these, with the appropriate database behind them, would allow a pathologist sort of an assist—a computational assist—in making key decisions. And that is reputedly a huge quality advantage.
Inspirata is transforming cancer diagnostics with computational image analysis. Learn more at https://www.inspirata.com/computational-image-analysis-and-decision-support-tools/.
