New Deep Learning Model for X-Ray Classification
- By John K. Waters
Processing platform provider Xilinx and health-care-focused artificial intelligence (AI) solutions company Spline.ai, today unveiled a fully functional medical X-ray classification deep learning (DL) model, and a reference design kit to go with it.
San Jose, CA-based Xilinx designs, develops, and markets programmable logic products, including integrated circuits (ICs) and software design tools. The company is probably best known as the inventor of the field-programmable gate array (FPGA), but also hardware programmable SoCs and the Application Configuration Access Protocol (ACAP) for storing and synchronizing general configuration and preference data. Spline.ai, which is based in India, makes AI tools and products for healthcare. It's flagship product, Dr. Spline, is a secure, healthcare-focused conversational UI platform for medical practitioners.
Integrated with Amazon Web Services (AWS) IoT Greengrass, the collaboratively developed solution uses an open-source model that runs on a Python programming platform on a Xilinx Zynq UltraScale+ MPSoC device--which means 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 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 companies said in a statement.
"AI is one of the fastest growing and highest demand application areas of healthcare, so we're excited to share this adaptable, open-source solution with the industry," said Kapil Shankar, vice president of marketing and business development in Xilinx's Core Markets Group. "The cost-effective solution offers low latency, power efficiency, and scalability. Plus, as the model can be easily adapted to similar clinical and diagnostic applications, medical equipment makers and healthcare providers are empowered to swiftly develop future clinical and radiological applications using the reference design kit."
The solution's artificial intelligence (AI) model is trained using Amazon SageMaker and is deployed from the cloud to the edge using AWS IoT Greengrass, enabling remote machine learning (ML) model updates, geographically distributed inference, and the ability to scale across remote networks and large geographies.
"Amazon SageMaker enabled Xilinx and Spline.AI to develop a high-quality solution that can support highly accurate clinical diagnostics using low cost medical appliances," said Dirk Didascalou, vice president of IoT at Amazon Web Services. "The integration of AWS IoT Greengrass enables physicians to easily upload X-ray images to the cloud without the need of a physical medical device, enabling physicians to extend the delivery care to more remote locations."
So far, the solution has been used for a pneumonia and Covid-19 detection system, the companies say, with high levels of accuracy and low inference latency. The development team leveraged more than 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.
John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS. He can be reached at firstname.lastname@example.org.