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A paradigm shift in patient specific care —

how UNiD™ ASI augments surgeon's performance

Planning has always been the key to successful surgery. With the introduction of UNiD™ Adaptive Spine Intelligence (ASI) to its portfolio in 2020, Medtronic strengthened the use of AI and patient specific care in planning, execution and analysis of spine surgery. UNiD™ ASI uses artificial intelligence based on a data set collected from thousands of surgeries for pre-operative planning of patient-specific spinal rods and cages as well as post-operative analysis.¹

Dr. Christopher J. Kleck of the University of Colorado, USA outlines how UNiD™ ASI caused a paradigm shift in his institution – away from aligning the rods to the patient and towards aligning the patient to the desired final posture as planned pre-operatively.

Individual machine bent rods – made to the surgeon’s order

Whereas previously, spinal rods were bent by hand with limited precision and reproducibility, UNiD™ ASI now provides precise industrially bent rods. Based on pre-operative images of the patient as well as parameters like age and precondition, the predictive AI software suggests various surgery plans and their likely outcome to the surgeon. Following the surgeon’s decision for a plan, the rods are then made patient-specific and to order.

For Dr. Kleck, the results represent a significant improvement in achievement of alignment goals with UNiD vs. hand-bent rods. He stressed that the precision of the predictive software outshines the performance of surgeons, giving the example of a patient who two years after surgery is 3-4 degrees in tilt away from pre-operative planning.²

In addition to support in rod-production and surgical planning, UNiD™ ASI technology offers analytical tools as well. Post-operative images help analyse the outcome and its benefit for the patient, allowing the surgeon to check whether his plan succeeded. In addition, they also feed into the data set, further increasing UNiD’s precision through machine learning.^3,4

References

Verification and validation test reports 20RAP029 and 20RAP027
Lee, Nathan J., et al. "Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented horacic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum?" Global Spine Journal (2020).
Technical instruction IT-UNI008
DOC101BE: Algorithm Design Document

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