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.

WO2025212778A1 Publication Date: 2025-10-09WARSAW ORTHOPEDIC INC
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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

Technical Problem

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.

Method used

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.

Benefits of technology

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.

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Abstract

Methods and systems are provided for using a machine learning system, such as an artificial neural network, with machine learning or deep learning recognition of clinical data patterns and correlations, with capabilities to integrate generative artificial intelligence, to determine the optimal bone grafting materials and procedures personalized to a patient and / or digital twin of a patient. In an embodiment describe herein, a representation of a location of a bone graft for a patient is accessed. A recommendation including an implant, one or more bone graft materials or biologics for the patient is generated based on applying a representation of the location of the bone graft and health data of the patient within a neural network. The recommendation is displayed at a provider user interface (UI) and / or patient UI.
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