Cloud Corneal Difference Mapping for Cross-Device Topography
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Solution Overview
Problem
Current corneal topography systems are large, non-portable, and expensive, limiting their use and market reach, and they cannot generate compatible data files for comparing topography examinations from different instruments, hindering early detection of corneal diseases like keratoconus.
Innovation Solution
A cloud-based difference mapping system using portable corneal topography devices like the Delphi system, which integrates with smartphones, generates compatible data files and automatically compares topography studies across different devices, notifying providers and patients of clinically relevant changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional corneal topography systems are used, then measurement precision is maintained, but device portability and accessibility deteriorate due to large size and high cost
Solution Approach 1:
The system separates the corneal topography measurement function into a portable handheld device that can be integrated into standard exam rooms, while computational processing and data storage are distributed to cloud-based servers. This segmentation allows the measurement device to be small and portable while maintaining diagnostic capability through cloud-based analysis.
Solution Approach 2:
A cloud-based processing server acts as an intermediary between multiple portable corneal topography devices and the diagnostic analysis. The server receives data files from different devices, standardizes the data format, and performs comprehensive analysis, enabling portable devices to achieve measurement precision comparable to traditional large systems.
2Adaptability or versatility
If different corneal topography systems are used, then device versatility and accessibility improve, but data compatibility and comparability deteriorate
Solution Approach 1:
The cloud-based processing server provides universal data handling capability that accepts data files from multiple different corneal topography device manufacturers. It standardizes various data formats into a common structure, enabling difference mapping and longitudinal comparison across devices that previously could not interchange data.
Solution Approach 2:
The server acts as a data translation intermediary, receiving proprietary data files from different device manufacturers, converting them to a standardized format, and enabling cross-device comparison. This intermediary function preserves data integrity while achieving universal compatibility.
3Measurement precision
If manual difference mapping processes are used, then measurement precision is maintained, but examiner workload and potential for human error increase
Solution Approach 1:
The system implements automated difference mapping that performs the comparison and analysis functions previously requiring manual examiner intervention. The server automatically retrieves historical data, performs difference mapping calculations, generates visual outputs, and identifies clinically significant changes, reducing examiner workload while maintaining precision through consistent algorithmic application.
Solution Approach 2:
The automated system provides feedback to examiners by highlighting clinically significant changes and generating actionable insights from difference maps. This feedback mechanism maintains measurement precision while improving productivity by reducing manual analysis time and minimizing human error in interpretation.
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
Enables early detection of corneal pathologies like keratoconus, reduces examiner errors, and expands access to high-quality diagnostic care globally, facilitating earlier treatment and preventing vision-threatening consequences.
Implementation Method 1
Placido reflectance topography systems...projects a specific illuminated source pattern to the eye being tested (typically a series of concentric illuminated rings) and captures an image of the ring reflections from the cornea
Data Source
AI summary
A method to perform automatic corneal topography or tomography difference mapping includes receiving one or more corneal topography or tomography data files and/or a corneal image for an examined patient from a corneal topography or tomography system; receiving personal identification parameters from captured user personal data communicated from the corneal topography or tomography system; and comparing received patient identification parameters to existing patient identification parameters in a database to identify if there are existing topography or tomography data files for a same patient in the database. The method may further include retrieving a prior topography or tomography data file for the patient from the database; and performing difference mapping by comparing the received topography or tomography data files to the prior topography or tomography data file retrieved from the database to generate a topography or tomography difference map.