The newly launched ML-based Simpleware AS Ortho is a module specifically designed for segmentation needs in the hips and knees. When applying this automated option with Synopsys' ScanIP software to run their analysis, users will easily see a 20 to 50 times faster rate of segmentation for clinical images. This revolutionary technology is fully scalable, and while helping to achieve more consistency and increased reliability in biomechanical compatibility, it can also dramatically streamline the workflow process in both pre-surgical planning and medical device design. Users will thus achieve significant cost-savings in the product development cycle.
"Image segmentation of MRI and CT scans presents a significant challenge for our surgical and engineering multidisciplinary teams. We're excited to collaborate with the Simpleware product group at Synopsys for solutions to this challenge," said Johann Henckel MD, Orthopedic Surgeon, Royal National Orthopaedic Hospital, UK. "What is currently a laborious process that occupies significant engineering resources and time can now be completed quickly, accurately and with less variability, promising a scalable solution for generating high-fidelity patient specific models, surgical tools and bespoke implants."
Based on research by the journal Orthopedic Surgery, total knee arthroplasties (TKAs) in the USA will grow from 719,000 in 2015 to 3.48 million by 2030, while total hip arthroplasties (THAs) will almost double from 332,000 to 572,000 in the same period. This new ML-based product offering is an exciting new step for Synopsys' role in these fast-growing healthcare medical markets.
"The demand for image-based modeling of human anatomy tools with ML-enabled intelligence is rapidly growing, especially in markets that include patient specific workflows for medical devices, surgical guides and planning, and in silico clinical trials," said Terry Ma, vice president of engineering at Synopsys. "We're looking forward to collaborating with more medical device companies to solve their long-standing image segmentation challenges."
