Personalized spinal implant and biologics
A machine learning model optimizes bone graft material selection by analyzing patient data and implant characteristics, addressing the lack of awareness in surgeons, improving clinical outcomes through personalized treatment.
Patent Information
- Application Number
- PCT/US2025/022754
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2024-06-20
- Filing Date
- 2025-04-02
- Publication Date
- 2025-10-09
AI Technical Summary
Surgeons lack awareness of the biochemical properties and efficacy of bone graft materials and implants, leading to suboptimal selection based on personal experience rather than patient-specific characteristics, which can affect clinical outcomes.
A machine learning model, such as an artificial neural network, is used to analyze patient data and implant characteristics to recommend optimal bone grafting materials and procedures, considering factors like osteoconduction, osteoinduction, and osteogenesis, to enhance personalized treatment.
The model provides data-driven recommendations that optimize clinical outcomes by aligning implant selection with patient-specific health characteristics, reducing pain, minimizing infection, and lowering costs.