Finite Element Eye Model Without Limbus Restraints
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Solution Overview
Problem
Current methods for evaluating and predicting the biomechanical properties of the eye, particularly in the context of surgical interventions, lack precision in modeling the cornea's behavior and its interactions with surrounding tissues, which is crucial for preventing destabilization and visual impairments in ectatic diseases and post-refractive surgery complications.
Innovation Solution
A system utilizing imaging data to generate a finite element model of the eye that includes no a priori restraints on the corneal limbus motion, allowing for the simulation of therapeutic interventions and prediction of their outcomes by altering biomechanical properties and geometry, thereby accounting for the interactions between the cornea, sclera, and other ocular structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a priori restraints are applied to corneal limbus motion in finite element models, then model stability is improved, but prediction accuracy of surgical outcomes deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining the biomechanical properties and geometric parameters of ocular tissues (cornea, sclera, corneal limbus) before surgical simulation. The finite element model is constructed with predetermined material properties, boundary conditions, and loading scenarios that represent physiological states, allowing the model to naturally predict post-surgical outcomes without requiring a priori restraints on corneal limbus motion.
2Productivity
If simplified models are used for surgical prediction, then computational speed is improved, but modeling precision of corneal-tissue interactions deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the ocular system into distinct finite element components: cornea, sclera, and corneal limbus, each with its own mesh and material properties. This segmentation allows the model to capture complex tissue interactions and boundary conditions while maintaining computational efficiency through modular structure and localized refinement where needed.
Solution Approach 2:
The patent applies local quality by assigning different material properties, mesh densities, and boundary conditions to different regions of the ocular tissues. The cornea, sclera, and corneal limbus are modeled with region-specific biomechanical characteristics, allowing accurate representation of local tissue behavior under surgical loads while optimizing overall computational performance.
3Measurement precision
If detailed finite element models with multiple tissue properties are used, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by systematically varying biomechanical parameters (Young's modulus, Poisson's ratio, thickness) and geometric parameters of ocular tissues to match patient-specific anatomy and tissue properties. The finite element model allows independent adjustment of material properties for cornea, sclera, and corneal limbus, enabling accurate prediction of surgical outcomes while managing complexity through parameterized definitions.
Data Source
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AI summary
Systems and methods are provided for predicting the results of a therapeutic intervention to an eye. An imaging system is configured to provide image data representing at least a portion of the eye of the patient. An input device is configured to permit a user to design a proposed therapeutic intervention for the eye of the patient. A finite element modeling component is configured to generate a finite element model representing the condition of the eye of the patient after the proposed therapeutic intervention according to the image data, the proposed therapeutic intervention, and at least one biomechanical property of tissue comprising the eye. The generated model is constructed as to have no a priori restraints on the motion of the corneal limbus. A display is configured to display the generated model to the user.