3D Robotic Spine ‘Twin’ Offers New Way To Preview Surgical Procedures

This new approach could provide surgeons with first-hand data to compare the effects of different surgical interventions to treat diseases of the spine before surgery and potentially reduce the rates of complication and failure of artificial disc implantation.

Degenerative disc disease affects about 40 percent of people aged 40, increasing to about 80 percent among those aged 80 or older. The disease, which is the deterioration of one or more intervertebral discs of the spine, often is surgically treated with cervical disc implants.

In order to determine if a patient is a candidate for a cervical disc implant, surgeons have to rely primarily on the findings of diagnostic imaging studies, without any input from biomechanical data to optimize the type of prosthesis. This may occasionally lead to complications and implant failure.

To address these problems, Florida Atlantic University’s Erik Engeberg, Ph.D., senior author of the study, and researchers from the College of Engineering and Computer Science , in collaboration with Frank Vrionis, M.D., senior author of the study and director of the Marcus Neuroscience Institute, part of Baptist Health, have created a novel robotic replica of a human spine to enable surgeons to preview the effects of surgical interventions prior to the operation.

The researchers have developed a 3D printed spine replica modified to include an artificial disc implant and outfitted with a soft magnetic sensor array. The Marcus Neuroscience Institute has its hub on Boca Raton Regional Hospital’s campus and satellite locations at Bethesda Hospital in Boynton Beach and Deerfield Beach.

The patient-specific robotic spine model was based on a CT scan of the human spine. A modified artificial disc was ‘implanted’ into the cervical spine replica and the soft magnet was embedded in the vertebra replica. A robotic arm flexed and extended the cervical spine replica while the intervertebral loads were monitored with the soft magnetic sensor array to classify the spine posture with four different machine-learning algorithms. The algorithms classified the amplitude and the locations that external loads were applied. Researchers then compared the capabilities of the algorithms to classify five different postures of the human spine robotic replica (center, mid-flexion, flexion, mid-extension and extension).

Results of the study, published in the journal Sensorsshowed that the soft magnetic sensor array system had the high capability to classify the five different postures of the spine with 100 percent accuracy, which can be a predictor of different problems of the spine that people experience. These results indicate that the integration of the soft magnetic sensor array within the artificial disc ‘implanted,’ robotically actuated spine replica has the potential to generate physiologically relevant data before invasive surgeries, which could be used preoperatively to assess the suitability of a particular intervention for specific patients.

BY Gisele Galoustian | Florida Atlantic University

Image Credit: Gisele Galoustian / Florida Atlantic University


About Peter Coffaro 489 Articles
A growth-driven and strategic executive, Peter Coffaro commands more than 25 years of progressive management success within the medical device industry. Recognized by the World Journal of Orthopedics, Exponential Healthtech, and MedReps.com as one of the top medical sales influencers in the industry; he has 10 years of combined sales management experience and has held positions as a Director, General Manager, Distributor, and Vice President. Peter has worked for some of the top orthopedic companies in the world - Zimmer, DePuy, and Stryker. He is also the founder of OrthoFeed: a popular blog that covers digital orthopedic news and emerging medical technologies. Peter is a three-time Hall of Fame award winner at Johnson and Johnson and has an extensive background in organizational development, business development, sales management, digital marketing, and professional education. Peter holds a B.S. degree in Biology and Chemistry from Northern Illinois University.

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