Artificial intelligence in radiology involves the training of deep learning neural networks on image data to enhance image quality or perform diagnostic tasks. A characteristic of trained AI networks is that, in many instances, the exact mechanism leading to software output is not evident, hence the need to monitor and optimize AI performance in clinical imaging.
Phantoms designed to validate AI software output.
We supply phantoms with anatomical detail and pathologies to validate and monitor AI software for clinical imaging.
AI quality control can accelerate and improve the deployment of reliable AI for better workflow efficiency and patient care.
- End-to-end verification of AI software along with the entire imaging chain
- Reproducibility and diagnostic performance under varying imaging conditions
- Benchmarking of network architectures and software solutions
9 liver lesions
imaging technology 1
AI detects 9 of 9 lesions
imaging technology 2
AI detects 4 of 9 lesions
Real phantom images used for AI training
Example of AI trained on phantom data to denoise images.
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