Patient-Specific Spinal Implant Design to Reduce Vertebral Subsidence
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Solution Overview
Problem
The subsidence of intervertebral implants into the vertebral endplates during post-operative periods leads to reduced disc height and adverse clinical outcomes, affecting mechanical stability in spinal surgeries.
Innovation Solution
A system and method utilizing predictive analytics and machine learning to design patient-specific intervertebral implants and surgical procedures that minimize the likelihood and magnitude of implant subsidence by analyzing patient data and reference data sets to optimize implant design and surgical plans based on individual patient anatomy and pathology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intervertebral implants are used for spinal fusion surgeries, then spinal stability and fusion outcomes are improved, but implant subsidence into vertebral endplates occurs causing reduced disc height and adverse clinical outcomes
Solution Approach 1:
The system changes key parameters of the implant including surface area, stiffness, and geometry based on patient-specific data. By optimizing these parameters, the implant achieves better load distribution and reduced subsidence while maintaining spinal stability. The predictive analytics identify optimal parameter combinations before implantation.
Solution Approach 2:
The implant design incorporates local quality variations with different regions having different surface areas, stiffness, or material properties. This allows specific zones of the implant to be optimized for load-bearing, bone integration, or disc height maintenance, reducing overall subsidence while maintaining spinal stability.
2Reliability
If patient-specific implants are designed using predictive analytics and machine learning, then the probability of subsidence is reduced, but the complexity of the surgical planning process increases
Solution Approach 1:
The system performs preliminary actions by conducting predictive analytics and machine learning analysis before the surgical procedure. This pre-planning identifies optimal implant parameters and surgical approaches, reducing intraoperative complexity. The complex computational work is completed in advance, allowing surgeons to follow optimized protocols during surgery.
Solution Approach 2:
The system creates digital copies and virtual models of the patient's spinal anatomy to test different implant configurations computationally. This virtual prototyping allows complex design iterations without physical complexity, enabling optimization of implant parameters before actual surgery through simulation and prediction algorithms.
Data Source
AI summary
Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated. The surgical procedure or medical device design can be generated based at least partially on one or more parameters associated with a reduced risk of one or more post-operative conditions.