Retina Laser Planning Datasets From Structured Patient Imaging
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Solution Overview
Problem
Existing laser treatment systems for the retina lack standardized and systematic methods for creating treatment plans, making it difficult to efficiently and accurately plan tissue treatments.
Innovation Solution
A system comprising a camera for continuous retina imaging, an image processing device for generating structured data, a personal data acquisition device, and a processing device for assigning planning datasets based on patient data, with manual and automated modification capabilities, to standardize and improve treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual treatment planning is performed without standardized methods, then treatment planning can be flexible and adaptable to individual cases, but the process becomes time-consuming and lacks consistency
Solution Approach 1:
The system changes the parameters of treatment planning by introducing standardized evaluation criteria and automated algorithms that process retinal images according to predefined parameters. This transforms the planning process from purely manual and variable to semi-automated and consistent, while still allowing manual adjustment when needed.
Solution Approach 2:
The system enables self-service treatment planning through automated algorithms that independently analyze retinal images, identify treatment areas, and generate planning proposals. This reduces the time burden on operators while maintaining reliability through standardized automated processes.
2Measurement precision
If comprehensive patient data and continuous imaging are collected, then treatment planning accuracy and safety are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex treatment planning process into distinct functional modules: image acquisition, image processing, data collection, analysis, and planning generation. Each module handles specific tasks independently, reducing overall system complexity while improving measurement precision through specialized processing at each stage.
Solution Approach 2:
The system introduces an intermediary image processing device that acts as a mediator between the camera and the treatment planning system. This intermediary processes and structures image data before it reaches the planning algorithm, reducing the complexity burden on the main system while enhancing planning accuracy.
3Productivity
If automated planning algorithms are used, then treatment planning time is reduced and efficiency increases, but flexibility for manual adjustment and expert judgment may be limited
Solution Approach 1:
The system implements dynamic treatment planning where the automation level can be adjusted based on case complexity. For standard cases, fully automated algorithms provide rapid planning. For complex or unusual cases, the system allows increased manual intervention and adjustment, providing flexibility while maintaining high productivity for routine cases.
Solution Approach 2:
The system incorporates feedback mechanisms where automated planning results are reviewed and can be adjusted by experts. The feedback from manual adjustments is used to refine and improve the automated algorithms over time, maintaining both speed and flexibility in an iterative improvement cycle.
Data Source
AI summary
The invention relates to a system for creating planning data sets (34, 35, 36, 37) for the tissue treatment of the retina (16) of the eye (4) of a patient by means of a laser (2), with a camera (6, 7) which is configured to continuously capture images of the retina, with an image processing device (19) which is configured to generate and provide current structured image data of the retina from images captured by the camera, with a personal data acquisition device (21, 22), which is configured to acquire and/or provide personal data of a patient, and with a processing device (20), which is designed to assign planning datasets for the tissue treatment to the current structured image data of the retina and to the patient's personal data, wherein specifications, in particular patterns or rules, for the assignment of planning data to image data and patient's personal data are stored in the processing device.