Medtronic receives FDA clearance for next-generation UNiD Spine Analyzer

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Medtronic has announced that it has received US Food and Drug Administration (FDA) 510(k) clearance for its UNiD Spine Analyzer v4.0 planning platform, which includes a new algorithm for degenerative spine procedures.

The algorithm leverages machine learning to help surgeons plan and personalise procedures for patients undergoing lower lumbar spine surgery and predicts spinal compensation mechanisms six months after the operation, say Medtronic.

The new update also includes enhancements to the paediatric and adult deformity algorithms predicting compensatory changes to the spine. Medtronic state that they are the first and only company to have FDA cleared predictive models for spine surgery.

The release comes with a new UNiD Hub patient-centric platform that enables surgeons to track patients throughout the perioperative care pathway and assess surgical results through long-term radiographic and patient-reported outcomes data collection.

Dan Wolf, a vice president and general manager within the firm’s Cranial and Spinal Technology business, said: “Patient by patient, our UNiD lab engineers have learned from more than 10,000 spine surgery cases to deliver greater insights to surgeons that lead to better patient alignment.

“It is truly exciting to share that we have expanded our UNiD ASI technology to include hardware and software solutions dedicated to helping spine surgeons treat degenerative spinal pathologies, where the majority of spine surgery is performed.”

The new UNiD ASI Degen Algorithm is designed to help surgeons achieve spinal alignment by more accurately planning procedures and predicting spinal alignment after six months.

Christopher Kleck, an orthopaedic spine surgeon at the University of Colorado (Boulder, USA), added: “Alignment matters for all spinal surgery—both short construct degen and long construct deformity cases. Planning all of these cases with my UNiD lab engineer ensures that my surgical plan is backed by artificial intelligence and clinically important predictive models to set my patients up for long-term success.”


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