Ophthalmic Microscope Visual Guidance Overlay for Dense Cataract Imaging
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Solution Overview
Problem
Current ophthalmic microscope systems face challenges in visualizing tissue structures, particularly in dense cataracts and retinal surgeries, due to poor red reflex illumination and the use of toxic dyes, which limits the ability to accurately gauge depth and identify anatomical features during surgeries.
Innovation Solution
A machine-learning-based system that processes intraoperative sensor data from ophthalmic microscope systems to identify and track anatomical features, generating a visual guidance overlay to highlight or annotate structures of interest, aiding surgeons during procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If toxic dyes such as Trypan blue or Indocyanine green are used to stain and visualize retinal membranes, then the ability to visualize semi-translucent tissue structures is improved, but patient safety deteriorates due to toxicity
Solution Approach 1:
The patent converts the harmful effect of blocking light (cataract opacity) into a beneficial contrast mechanism. By using machine learning to analyze variations in light transmission through the cataractous lens, the system creates virtual staining effects that highlight anatomical structures without requiring toxic dyes. The 'harm' of the cataract blocking red reflex light is transformed into the basis for computational contrast enhancement.
Solution Approach 2:
The patent creates a digital copy of the anatomical structures through machine learning analysis of intraoperative images. Instead of relying on physical staining with toxic dyes, the system generates synthesized visual representations of retinal membranes and other structures by analyzing light transmission patterns and training models to predict and highlight anatomical features, thereby eliminating the need for harmful substances.
2Measurement precision
If steroids are used to stain transparent vitreous white to ensure complete removal of vitreous pockets, then the ability to detect vitreous structures is improved, but patient safety deteriorates due to toxicity
Solution Approach 1:
The patent replaces the chemical staining mechanism (steroids binding to vitreous proteins) with a computational analysis system. Machine learning algorithms process intraoperative images to detect and highlight vitreous structures based on light transmission characteristics, substituting the mechanical/chemical staining process with digital image processing and pattern recognition.
3Measurement precision
If red reflex illumination is used to visualize capsule, lens and anterior chamber structure, then the contrast to visualize these structures is improved, but the ability to penetrate dense cataracts deteriorates
Solution Approach 1:
The patent introduces machine learning as an intermediary between the captured images and the anatomical visualization. The system processes the raw intraoperative images through trained models that can extract and enhance anatomical structures even when the original light transmission is degraded by cataracts, acting as a computational mediator that bridges the gap between poor illumination and the need for clear visualization.
Solution Approach 2:
The patent changes the parameters of image analysis by transforming the approach from direct optical contrast (relying on red reflex light penetration) to computational contrast (using machine learning to analyze light transmission patterns). This parameter change allows the system to extract anatomical information even when the physical light transmission is blocked by dense cataracts.
4Illumination intensity
If endo-illumination system is used for retinal surgery, then the ability to illuminate posterior structures is improved, but the differentiation of ocular features deteriorates due to poor lighting and semi-translucent tissue
Solution Approach 1:
The patent implements a feedback loop where machine learning models continuously analyze intraoperative images and provide real-time guidance overlays. The system processes the visual information, identifies anatomical features, and feeds back enhanced visual representations to the surgeon, creating a feedback mechanism that compensates for the poor differentiation caused by semi-translucent tissue and suboptimal illumination.
Data Source
AI summary
Examples relate to an ophthalmic microscope system and to a corresponding system, method and computer program for an ophthalmic microscope system. The system comprises one or more processors and one or more storage devices. The system is configured to obtain intraoperative sensor data of an eye from at least one imaging device of the ophthalmic microscope system. The system is configured to process the intraoperative sensor data using a machine-learning model. The machine-learning model is trained to output information on one or more anatomical features of the eye based on the intraoperative sensor data. The system is configured to generate a display signal for a display device of the ophthalmic microscope system based on the information on the one or more anatomical features of the eye. The display signal comprises a visual guidance overlay for guiding a user of the ophthalmic microscope system with respect to the one or more anatomical features of the eye.