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Proprio Vision

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Proprio Vision

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Proprio Vision's Paradigm™ is an AI-driven surgical navigation platform designed to enhance precision and efficiency in the operating room. By integrating advanced sensors, computing power, and artificial intelligence, Paradigm captures multi-modal surgical views to construct real-time 3D models of patient anatomy. This innovative approach aims to improve surgical accuracy, streamline workflows, and reduce reliance on radiation-based imaging. Key Features and Functionality: - Segmental Registration: Utilizes computer vision and light-field imaging to continuously track anatomy at the segmental level, adapting to changes in the surgical field without the need for repeated imaging. - On-Demand Measurements: Provides radiation-free intraoperative measurements of spinal alignment, disc height, and segmental angles, enabling surgeons to assess and adjust in real-time without interrupting the procedure. - Data Applications: Generates structured, standardized data during each procedure, supporting longitudinal learning, objective documentation, training initiatives, and the development of a comprehensive surgical data network. Primary Value and Problem Solved: Paradigm addresses the limitations of traditional surgical navigation systems, which often rely on static images and fixed landmarks, making it challenging to adapt to the dynamic 3D environment of surgery. By offering continuous 3D visualization and real-time data, Paradigm enhances surgical precision, reduces workflow interruptions, and minimizes radiation exposure. This leads to improved patient outcomes, increased surgical efficiency, and the potential for higher reimbursement rates due to enhanced procedural accuracy.

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What is Proprio Vision?

Proprio Vision's Paradigm™ is an AI-driven surgical navigation platform designed to enhance precision and efficiency in the operating room. By integrating advanced sensors, computing power, and artificial intelligence, Paradigm captures multi-modal surgical views to construct real-time 3D models of patient anatomy. This innovative approach aims to improve surgical accuracy, streamline workflows, and reduce reliance on radiation-based imaging.

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Year Founded
2016