Retinal Thickness Analysis Using Ocular Elongation Data
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Solution Overview
Problem
Existing technologies for analyzing retinal layer thickness in eye tomographic images require manual switching of statistical databases based on ocular axial length, which is inefficient and limits the ability to accurately diagnose diseases like glaucoma.
Innovation Solution
An information processing apparatus and method that acquire elongation state information and retinal layer thickness data, using a trained model to analyze abnormalities in retinal layer thickness based on the acquired information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual switching of statistical databases based on ocular axial length is used, then diagnostic accuracy can be maintained through class-based analysis, but analysis efficiency deteriorates due to manual operation requirements
Solution Approach 1:
The system automatically selects the appropriate statistical database and performs class-based analysis without requiring manual intervention. The automated selection process matches the patient's ocular axial length to the corresponding statistical database class, enabling the system to serve itself in terms of database selection and analysis execution, thereby maintaining diagnostic accuracy while eliminating manual operation requirements
Solution Approach 2:
The system dynamically adapts the analysis process based on the patient's specific ocular axial length. By automatically adjusting which statistical database is applied based on real-time measurement data, the system creates a dynamic analysis workflow that optimizes both accuracy and efficiency for each individual case rather than using a static manual process
2Reliability
If class-based statistical databases are prepared in advance, then analysis reliability is improved through standardized comparison, but system complexity increases due to multiple database management requirements
Solution Approach 1:
The system employs a universal database management structure that handles multiple statistical databases through a single integrated interface. This universal system can select and apply different databases based on ocular axial length classes without requiring separate manual management processes, thereby maintaining analysis reliability through standardized comparison while reducing the operational complexity of managing multiple databases
Solution Approach 2:
The automated selection mechanism acts as an intermediary between the patient's measurement data and the multiple statistical databases. This intermediary automatically matches the appropriate database based on ocular axial length, eliminating the need for complex manual database management while ensuring reliable standardized comparison through consistent automated selection
3Productivity
If automated analysis using trained models is implemented, then analysis efficiency is improved through automation, but measurement precision may deteriorate without manual verification steps
Solution Approach 1:
The automated analysis system incorporates feedback mechanisms where the trained model's output is continuously refined based on comparison with established statistical databases. The system provides automated feedback loops that verify measurement results against class-based statistical norms, ensuring measurement precision is maintained while achieving high analysis efficiency through automation without requiring manual verification steps
Data Source
AI summary
Provided is an information processing apparatus including: an elongation information acquisition unit configured to acquire information regarding an elongation state of an eyeball to be analyzed; a data acquisition unit configured to acquire data including information regarding a thickness of a retinal layer of the eyeball; and an analysis unit configured to analyze an abnormality in the thickness of the retinal layer based on the information regarding the elongation state and the data including the information regarding the thickness of the retinal layer. The analysis unit includes a trained model configured to use, as input, at least the data including the information regarding the thickness of the retinal layer to output information regarding the abnormality in the thickness of the retinal layer.