At NVIDIA, we have the privilege of working with companies that are changing how care is delivered. We’re an accelerated computing company, but our partners bring us into problems I might never learn about otherwise.
For Breast Cancer Awareness Month, @NVIDIA Inception is highlighting four companies working on different parts of breast cancer care.
At @isonohealth, researchers are working to solve for the variation in handheld ultrasound between different operator’s technique, which makes comparing scans across visits difficult. Their automated system captures a standardized scan in about two minutes, giving clinicians a more repeatable view to track changes.
@whiterabbitai is tackling the workload of breast cancer screening, where radiologists review large numbers of mammograms to find relatively few cancers. Alongside its breast-density assessment software, the team is researching how AI could identify negative scans automatically and help radiologists focus on cases needing closer review.
Then there’s the question of what to do after a diagnosis. @Ataraxis_AI uses pathology slides and clinical data already collected during care to help assess recurrence risk and whether chemotherapy is likely to benefit a patient. This means getting more from existing data and reducing extra scans.
@SimBioSys focuses on treatment planning. Surgeons need to understand how a tumor sits relative to the tissue around it. The company builds 3D models that make those relationships easier to visualize and help inform surgical decisions.
More on each of these companies and how NVIDIA is accelerating their work here: blogs.nvidia.com/blog/ai-breast…
For Breast Cancer Awareness Month, @NVIDIA Inception is highlighting four companies working on different parts of breast cancer care.
At @isonohealth, researchers are working to solve for the variation in handheld ultrasound between different operator’s technique, which makes comparing scans across visits difficult. Their automated system captures a standardized scan in about two minutes, giving clinicians a more repeatable view to track changes.
@whiterabbitai is tackling the workload of breast cancer screening, where radiologists review large numbers of mammograms to find relatively few cancers. Alongside its breast-density assessment software, the team is researching how AI could identify negative scans automatically and help radiologists focus on cases needing closer review.
Then there’s the question of what to do after a diagnosis. @Ataraxis_AI uses pathology slides and clinical data already collected during care to help assess recurrence risk and whether chemotherapy is likely to benefit a patient. This means getting more from existing data and reducing extra scans.
@SimBioSys focuses on treatment planning. Surgeons need to understand how a tumor sits relative to the tissue around it. The company builds 3D models that make those relationships easier to visualize and help inform surgical decisions.
More on each of these companies and how NVIDIA is accelerating their work here: blogs.nvidia.com/blog/ai-breast…
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