problem
While there are models for analyzing static images such as CT images, analyzing endoscopic data is difficult because the data is in the form of a video.
use case
- Using the NeoPulse® video analysis capabilities, a model was created to analyze endoscopic data
- In real-time, endoscopy data was fed into the model and a diagnosis was made along with associated accuracy
How to Customize
- To build a customized version of this model, NeoPulse® will need a large enough sample of endoscopy videos for each label (ex. Pathological vs. Non-pathological)
- Applicable to clinical/diagnostic and research areas
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.
Deep learning detection of prostate cancer recurrence with 18F-FACBC (fluciclovine, Axumin®) positron emission tomography