Data normalization/customization engine for medical imaging workflows
A platform that presents harmonized images from diverse CT data tailored for any radiologist, researcher, or AI tool.
Problem Examples
Clinical Variability
Who doesn't like normalized data?
Radiologist mind space drain
When CT images are reconstructed differently from scan to scan, it can be difficult for radiologists to determine if changes in a patient's disease or healing process are real or simply the result of a different CT scanner or reconstruction method being used.
"I've had to play guessing games every time I open something from a referring center."
-Former NIH and current cancer center radiologist
Radiologist mind space drain
Who doesn't like normalized data?
Radiologist mind space drain
When CT images are reconstructed in different ways, radiologists must first decipher the type of image they are looking at before they can interpret the clinical data contained within the image.
Instead of practicing medicine, radiologists waste valuable time sorting through images.
"[RadUnity's solution] would be a huge time savings but i
When CT images are reconstructed in different ways, radiologists must first decipher the type of image they are looking at before they can interpret the clinical data contained within the image.
Instead of practicing medicine, radiologists waste valuable time sorting through images.
"[RadUnity's solution] would be a huge time savings but it would also be a huge emotional or brain real estate savings, just because of the burnout caused by thinking, ‘Oh man, I got to hang this every single time’"
-Fellowship trained academic radiologist
Who doesn't like normalized data?
Who doesn't like normalized data?
Who doesn't like normalized data?
AI vendors that assist radiologists in interpreting images are also impacted by this inconsistency, as they must expend energy building processes capable of ingesting a large variety of non-standardized medical images.
"Comparing images never works, because if you get something from somewhere else, or even if it's from your same hospital
AI vendors that assist radiologists in interpreting images are also impacted by this inconsistency, as they must expend energy building processes capable of ingesting a large variety of non-standardized medical images.
"Comparing images never works, because if you get something from somewhere else, or even if it's from your same hospital system, it is not going to hang up properly."
-Academic radiologist