Predictive Ophthalmic Apparatus for Intraoperative Stress Analysis
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Solution Overview
Problem
Ophthalmic surgeries, particularly those involving epiretinal membrane removal, face challenges due to changes in the eye's condition between the initial examination and surgery, requiring real-time assistance for physicians to adjust surgical plans and navigate complex situations effectively.
Innovation Solution
A system and method utilizing quasi-real time imaging and computational models to assist physicians during surgery by determining recommended regions and procedures based on intraocular pressure and stress concentrations, providing immediate feedback on expected results, allowing for dynamic adjustments during the procedure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a surgical plan is created based on pre-operative examination images, then the surgical procedure can be planned in advance, but the eye condition may change significantly between examination and surgery requiring plan modifications
Solution Approach 1:
The system performs preliminary actions by capturing images and determining stress concentrations and recommended procedures before surgery begins. The computational model pre-calculates potential surgical approaches based on pre-operative imaging, allowing the physician to have a prepared plan while accounting for potential changes during the procedure.
Solution Approach 2:
The system implements feedback by continuously updating the computational model with new imaging data acquired during surgery. The apparatus compares real-time images with the pre-established surgical plan, providing feedback on whether the plan remains appropriate or needs modification based on current eye conditions, thus maintaining plan reliability despite temporal changes.
2Manufacturing precision
If quasi-real time imaging and computational modeling are used during surgery, then surgical precision and effectiveness are improved, but device complexity increases
Solution Approach 1:
The imaging apparatus is designed to perform multiple functions: capturing pre-operative images, acquiring intraoperative images, processing images through computational models, determining stress concentrations, and providing surgical recommendations. This multi-functionality reduces the need for separate specialized devices while maintaining surgical precision.
Solution Approach 2:
The computational model acts as an intermediary between the raw imaging data and the surgical decision-making process. It translates complex image data into actionable information about stress concentrations and recommended procedures, simplifying the interface between the complex imaging system and the physician without sacrificing precision.
3Ease of operation
If the physician relies on pre-operative images and surgical plans, then the initial surgical approach can be established, but the complexity of determining the next step during surgery increases
Solution Approach 1:
The system provides self-service by automatically analyzing intraoperative images and generating recommendations for the next surgical steps. The computational model independently processes imaging data to identify stress concentrations and suggest appropriate procedures, reducing the cognitive burden on the physician and simplifying intraoperative decision-making.
Solution Approach 2:
The computational model performs preliminary analysis of potential surgical approaches based on pre-operative imaging before the surgeon needs to make decisions during surgery. This pre-processing of information simplifies the intraoperative decision-making process by having options already evaluated and presented to the physician.
Data Source
Figure 1~2A
Figure 2B~2D
Figure 3
AI summary
A method and system assist a physician in performing an ophthalmic surgery. The method includes receiving a quasi-real time image of at least a first portion of the eye. The at least the first portion of the eye includes an operating field for the ophthalmic surgery. A recommended next region and a recommended next procedure are determined based on the quasi-real time image and a computational model of the eye. An expected next result for the recommended next procedure is calculated using the quasi-real time image and the computational model. The recommended next region, the recommended next procedure and the expected result are provided to the physician.