Automated Purkinje Image Detection for Precise IOL Alignment
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Solution Overview
Problem
Existing ophthalmic surgery, particularly cataract surgery, faces challenges in accurately aligning intraocular lenses (IOLs) with the visual axis of the eye, which affects surgical precision and outcomes.
Innovation Solution
An ophthalmic microscope system equipped with high-resolution cameras and machine learning models is used to segment and analyze Purkinje images, enabling precise alignment of IOLs by estimating their offset from the visual axis through a controller that processes images to provide real-time alignment feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment methods are used for IOL placement, then surgical simplicity is maintained, but alignment precision deteriorates
Solution Approach 1:
The system creates optical copies (Purkinje images) of the IOL and eye structures through camera imaging, then processes these copies through machine learning models to determine alignment. This allows precise measurement without adding physical complexity to the surgical procedure itself.
Solution Approach 2:
The patent replaces manual mechanical alignment methods with an automated optical and computational system. Cameras capture images, machine learning models process the data, and the system automatically determines IOL alignment, substituting human manual assessment with automated image analysis.
2Measurement precision
If automated image analysis is implemented, then alignment accuracy is improved, but processing time increases
Solution Approach 1:
The machine learning models are trained in advance on large datasets of eye images and IOL configurations. This preliminary training allows the models to rapidly process intraoperative images without requiring complex real-time computations, reducing processing time during surgery.
Solution Approach 2:
The system uses optical copying through Purkinje image formation to create simplified representations of the IOL and eye structures. These optical copies are easier and faster to process than direct 3D analysis, enabling rapid automated alignment assessment.
3Difficulty of detecting and measuring
If traditional alignment methods are used, then equipment simplicity is maintained, but detection capability deteriorates
Solution Approach 1:
The system introduces Purkinje images as an intermediary representation between the physical IOL and the measurement system. These optical intermediaries make the IOL position and orientation visible and measurable, solving the detection problem without requiring direct physical contact or complex 3D sensing.
Solution Approach 2:
The machine learning models analyze multiple parameters from the images (position, size, shape, orientation of Purkinje images) and transform this visual information into precise alignment measurements. This parameter transformation enables detection capabilities that would be impossible with traditional visual assessment alone.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates accurate and efficient alignment of IOLs during surgery, improving surgical precision and outcomes by providing real-time alignment feedback.
Implementation Method 1
Light from the illuminator optics is incident on the eye and reflected Purkinje images from the eye are captured by the camera
Data Source
AI summary
A system includes an ophthalmic microscope including a camera and a controller coupled to the camera. The controller configured to receive at least one image from the camera, the at least one image including a representation of an eye of a patient. The controller segments the at least one image using a machine learning model to obtain at least one segmented image including one or more labels of one or more Purkinje images represented in the at least one image. The controller produces an output according to the at least one segmented image. The output may be an estimate location of the visual axis of the eye. The output may include an estimate of an offset between the visual axis and a center of an IOL implanted in the eye. The segmented image may further label the IOL and possibly one or more items of anatomy of the eye.