Xilinx has introduced a medical X-ray classification deep-learning model and a reference design kit in association with Spline.AI on Amazon Web Services (AWS).

The model is deployed on the Xilinx Zynq UltraScale+ MPSoC device based ZCU104 and leverages the Xilinx deep learning processor unit (DPU), a soft-IP tensor accelerator, which is powerful enough to run a variety of neural networks, including classification and detection of diseases.

It uses an open-source model, which runs on a Python programming platform on a Xilinx Zynq UltraScale+ MPSoC device, meaning it can be adapted by researchers to suit different application specific requirements.

Medical diagnostic, clinical equipment makers and healthcare service providers can use the open-source design to rapidly develop and deploy trained models for many clinical and radiological applications in a mobile, portable or point-of-care edge device with the option to scale using the cloud.

The model has been used for a pneumonia and Covid-19 detection system.

The development team leveraged over 30,000 curated and labeled pneumonia images and 500 Covid-19 images to train the deep learning models.

This data is made available for public research by healthcare and research institutes such as National Institute of Health (NIH), Stanford University, and MIT, as well as other hospitals and clinics around the world.