OCT Interferogram Analysis Using AI for Accurate Eye Diagnostics
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Solution Overview
Problem
Existing OCT image processing methods suffer from information loss and reduced accuracy due to signal processing, leading to inefficient and less reliable analysis results for eye diagnostics.
Innovation Solution
An ophthalmic information processing apparatus that utilizes a learned model generated through machine learning to directly generate medical service supporting information from interferograms acquired via OCT scan, bypassing complex signal processing to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used to process OCT images, then image formation is achieved, but information loss occurs and measurement precision deteriorates
Solution Approach 1:
The patent extracts and utilizes only the essential interferogram data directly for diagnosis, bypassing the traditional intermediate step of converting to OCT images through complex signal processing. This extraction approach preserves information that would otherwise be lost in the processing chain while achieving diagnostic accuracy.
Solution Approach 2:
The patent introduces an intermediary approach by using machine learning models that can directly process interferograms without requiring traditional OCT image conversion. This intermediary method (direct interferogram processing via ML) eliminates the harmful intermediate processing steps that cause information loss while maintaining diagnostic capability.
2Measurement precision
If complex signal processing is performed to generate OCT images, then image quality is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent extracts the essential diagnostic information directly from interferograms, eliminating the need for time-consuming complex signal processing steps traditionally required to generate OCT images. This direct extraction approach maintains diagnostic quality while significantly improving processing efficiency.
Solution Approach 2:
The patent replaces the traditional mechanical signal processing system (Fourier transforms, filtering, image reconstruction) with a machine learning-based system that can directly process interferograms. This substitution eliminates complex computational steps while maintaining or improving diagnostic accuracy and processing speed.
3Difficulty of detecting and measuring
If traditional OCT image processing is used, then diagnostic analysis is performed, but reliability of analysis results decreases due to information loss
Solution Approach 1:
The patent extracts diagnostic information directly from the原始 interferogram data, preserving all information content that would be lost in traditional image processing. This direct extraction ensures higher reliability of analysis results by eliminating the cumulative errors and information loss inherent in multi-step processing pipelines.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that can directly map interferogram data to diagnostic conclusions without requiring traditional image conversion. This intermediary approach maintains the integrity of the original data while providing reliable diagnostic analysis, eliminating the unreliability caused by traditional processing chain limitations.
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
The approach provides accurate and precise analysis results with reduced information loss, enabling efficient generation of medical service supporting information for eye diagnostics.
Implementation Method 1
Optical coherence tomography (OCT) apparatuses that are used to form images representing the surface morphology or the internal morphology of an object to be measured using light beam emitted from a laser light source or the like have been known
Implementation Method 2
acquire one or more interferograms obtained by performing OCT scan on an eye of an examinee
Data Source
AI summary
An ophthalmic information processing apparatus includes an acquisition unit and an information processor. The acquisition unit is configured to acquire one or more interferograms obtained by performing OCT scan on an eye of an examinee. The information processor is configured to execute generation processing of medical service supporting information that supports a provision of a medical service for the examinee, based on the one or more interferograms. The information processor is configured to execute at least a part of the generation processing using a learned model generated in advance by performing machine learning.