Ophthalmic Spectral Analysis Using Separate Deep Learning Models
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Solution Overview
Problem
Current MSI/HSI data interpretation for ophthalmology applications is subjective, time-consuming, and error-prone due to reliance on manual estimation by clinicians, making disease diagnosis cumbersome and unreliable.
Innovation Solution
A framework that applies image analytics and AI/ML techniques to process spectral and spatial information separately, using deep learning models to extract meaningful features for disease diagnosis, generating interpretable images and diagnostic outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual estimation by clinicians is used to interpret MSI/HSI data, then subjective disease diagnosis can be performed, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical estimation process by clinicians with an automated image analytics system using AI/ML techniques. The system automatically processes spectral and spatial information from MSI/HSI data through deep learning models, eliminating the time-consuming manual analysis while maintaining or improving diagnosis reliability through consistent algorithmic evaluation.
Solution Approach 2:
The system enables self-service diagnosis by automating the interpretation of complex spectral data without requiring extensive manual clinical estimation. The deep learning models independently extract features and generate diagnostic outputs, allowing the system to serve itself in analyzing the data while providing reliable disease detection results.
2Reliability
If manual interpretation of complex spectral information is performed, then disease diagnosis can be attempted, but the process becomes cumbersome and unreliable
Solution Approach 1:
The patent substitutes the complex manual interpretation process with an automated computational system. The image analytics platform uses AI/ML algorithms to automatically extract spectral and spatial features from MSI/HSI data, generating reliable diagnostic outputs without the cumbersome manual steps that previously reduced ease of operation.
Solution Approach 2:
The system introduces an intermediary layer of deep learning models that mediate between the complex spectral data and the final diagnosis. These models automatically process and interpret the spectral and spatial information, bridging the gap between raw data and clinical interpretation while improving both reliability and ease of operation.
3Extent of automation
If deep learning models are used to process spectral and spatial information separately, then automated disease detection can be achieved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex processing task into separate modules: one deep learning model processes spectral information while another processes spatial information. This modular approach enables automated disease detection by handling different aspects of the data independently, then combining results to achieve high automation while managing system complexity through organized functional separation.
Data Source
AI summary
In certain embodiments, an ophthalmic system and computer-implemented method for analyzing multiple spectral information to generate ophthalmic information are described. In an exemplary ophthalmic system, multiple spectral information associated with an eye of a patient is captured via an imaging system. A first set of information and a second set of information are extracted from the multiple spectral information. A visualization of the multiple spectral information is generated using the first set of information. The first set of information and the second set of information are evaluated using different deep learning models to generate ophthalmic information. The ophthalmic information is sent to a user for diagnostic evaluation.