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RSIP Vision Launches AI-Driven Solution for Knee Replacement Patients

by Fred Pennic 05/29/2019 Leave a Comment

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RSIP Vision Launches AI-Driven Solution for Knee Replacement Patients

RSIP Vision, a provider artificial intelligence (AI), computer vision, and image processing technology, has announced the release of a new AI module that promises a life-changing impact on the millions of patients who undergo knee surgery every year. For most, knee replacement surgery is a difficult option, with a painful recovery and a long, rigorous rehabilitation period. Now the superior accuracy and precision of the RSIP Vision AI solution, unmatched in the market today, revolutionizes knee surgery to provide remarkably improved outcomes for patients. By helping surgeons match patients with the best possible implants, the groundbreaking technology offers patients far greater comfort, less pain, and a faster road to full mobility.

The innovative AI solution, developed explicitly for knee surgeries and based on advanced CT segmentation technology, represents a dramatic shift in the industry. Deployed by medical device companies, the AI technology supports doctors and radiologists with a faster, more exacting pre-operative process for knee replacement surgery—one of the most prevalent orthopedic surgeries in the U.S. today. Since implants often last for more than 20 years, the AI technology contributes to a knee patient’s higher quality of life for many years to come.

The new module is the first in a series of advanced solutions that RSIP Vision develops based on advanced AI segmentation and computer vision technologies. When integrated into the software environments of medical devices, these modules enhance capabilities and outcomes in various fields, including orthopedics, pulmonology, and cardiology. The AI modules solve complex technological challenges and add value to these vendor offerings by ensuring greater precision, shorter time to market, and lower costs.

Impact of Knee Replacement on Patients

Knee replacement, also called total knee arthroplasty (TKA), is a surgical option for patients with painful knee damage typically caused by arthritis or injury. Damaged cartilage and bone are removed from the knee joint and replaced with an implant made of metal, plastic, or ceramic. The new generation of AI technologies are trained on thousands of patient records to learn what different bones look like, streamlining and accelerating all aspects of the complex surgery for both radiologists and surgeons.

New AI Module To Analyze Patient’s CT Scans More Efficiently

RSIP Vision’s new AI module enables radiologists to analyze patient’s CT scans far more efficiently, and produce exact measurements for every part of the body that makes contact with the implant. This type of precision is crucial to finding the right implant for each patient, whether choosing from a manufacturer’s selection or printing a personalized implant to match the knee perfectly. The module eliminates tedious effort and saves considerable time for radiologists, while also improving the speed and accuracy of their results.

This new AI technology also facilitates the planning process for surgeons, helping them find the right match for each implant and showing them exactly where to cut the bones. The AI results are applicable to robotic surgery as well since they clearly define the correct regions for cutting bones and indicate boundaries for drilling, decreasing the risk of  harm to the patient.

Growing Adoption of AI in Radiology

“As AI grows increasingly essential to every industry, we’re dedicated to solving technology challenges for a wide range of businesses,” RSIP Vision founder and CEO Ron Soferman said. “In bringing our expertise in deep learning, image processing, and computer vision to knee replacement surgery, it’s particularly gratifying to know that our AI module makes a tremendous difference in patients’ comfort and safety during a very stressful experience.”

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Tagged With: AI, Artificial Intelligence, Deep Learning, Machine Learning, Medical Device, Medical Devices, radiology, risk, Robotic Surgery

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