problem
Physicians are increasingly overworked, which can lead to misdiagnosis.
use case
• Using the NeoPulse3D Imaging analytics capability, a model was created by Stanford Medical School using anonymized patient data
• The model could classify a DICOM image into one or more of a set of classifications
• Results were announced at the RSNA 2018
• Model could be used to support a diagnosis – a “second opinion”
How to Customize
To build a customized version of this model, NeoPulse® will need a large enough sample of radiology images for each label (ex. Pathological vs. Non-pathological)
Research
To read all of our research performed by Stanford Medical, please visit our White Papers and Publications page.
We were able to successfully train an AI model to recognize complex industrial parts using Neopulse 3.0 on AWS. The AI solution was built very quickly and was able to recognize objects in unpredictable, real-world environments with high accuracy.
Everybody seems confident in AI, and they actually enjoy solving various AI problems.
The vendor’s services are integral to providing AI solutions for a wider audience. They had an effective project management style, accented by a quick working style.
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