Minerva: Patient-Specific Biomechanical Planning for Soft-Tissue Reconstruction and Arthroplasty

Minerva is an automated analysis platform that turns a routine pre-operative joint or organ scan (CT/MRI) into a personalized biomechanical model of the patient’s anatomy.

At its core, Minerva represents a powerful synthesis of Physical AI and Causal AI. By fusing deep learning with the immutable laws of mechanics, the platform models soft-tissue elasticity, multi-body kinematics, and real-world biomechanical loads. Beyond simulation, Minerva’s causal AI integration moves past simple correlation to reason about counterfactuals and interventions, actively modeling cause-and-effect relationships to answer critical clinical “what-if” questions (e.g., “If we shift the implant alignment by 2 degrees, how will it causally alter long-term cartilage stress, ligament tension, and joint longevity?”).

Built on a universal, highly flexible architecture, Minerva is not restricted to a single joint. The framework is designed to ingest multi-modality imaging for any joint or organ system in the human body, autonomously understanding what it finds and interpreting complex structures. By evaluating how anatomy naturally moves through minimum energy kinematics testing, Minerva determines the body’s optimal pathways of least resistance—predicting realistic, physiologically accurate movement and load distribution.

By characterizing living structures—bone quality, cartilage, soft tissues, and muscle—and mapping true causal trajectories, Minerva forecasts how a patient’s anatomy will physically behave through real-world activities, both as it is today and under various surgical or therapeutic scenarios.