Dry-Eye Dataset Generation for Standardized ML Classification
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Solution Overview
Problem
Current methods for diagnosing and treating dry-eye disease lack standardized methodologies to classify and quantify the extent of the disease, leading to inefficiencies in selecting appropriate therapies, as different types of dry-eye disease respond better to specific treatments.
Innovation Solution
A computerized method using machine-learning models that analyze a set of well-structured patient data, including various features, to classify and stratify dry-eye disease, providing tailored treatment recommendations based on machine-learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are used to classify and stratify dry-eye disease, then diagnostic accuracy and treatment recommendations are improved, but data processing complexity and model training requirements increase
Solution Approach 1:
The patent segments the data processing pipeline into distinct modules: data collection from multiple sources, data cleaning and validation, feature extraction, model training, validation, and deployment. This segmentation allows complex ML operations to be broken down into manageable steps, reducing overall system complexity while maintaining high diagnostic accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediate processing layers including data cleaning modules, feature extraction functions, and validation frameworks that act as mediators between raw data and final model outputs. These intermediaries preprocess and prepare data, filtering out noise and inconsistencies before they reach the ML models, thereby reducing the complexity burden on the models themselves while improving diagnostic precision.
2Productivity
If standardized methodologies are implemented for data analysis and classification, then treatment selection efficiency is improved, but implementation complexity and resource requirements increase
Solution Approach 1:
The patent standardizes data collection by defining specific parameters and measurement criteria for dry-eye disease assessment. It establishes standardized feature sets, classification categories, and validation metrics that can be consistently applied across different clinics and patients. This parameter standardization enables efficient automated treatment selection while managing implementation complexity through clear, prescriptive guidelines for data collection and processing.
Solution Approach 2:
The patent designs a universal ML framework that can handle multiple dry-eye disease subtypes and treatment scenarios within a single integrated system. The model architecture and data processing pipelines are designed to be multi-functional, accommodating various input formats and clinical scenarios without requiring separate specialized systems for each treatment type, thereby improving treatment selection efficiency while reducing overall implementation complexity.
Data Source
AI summary
In one aspect, a computerized method includes the step of obtaining a set of eye data of a patient from a medical practitioner in a computer input form. The method includes the step of acquiring a set of dry eye patient data from a set of well-structured dry eye patient data samples, wherein each sample dry eye patient data comprises a plurality of features. The method includes the step of identifying the plurality of data features in the set of well-structured dry eye patient data samples. The method includes the step of implementing a data cleaning process on the set of well-structured dry eye patient data samples. The method includes the step of implementing a feature selection on the set of well-structured dry eye patient data samples, wherein the feature selection comprises selecting a subset of relevant features for machine-learning model construction. The method includes the step of providing a specified machine-learning (ML) model. The method includes the step of training the ML model with the set of well-structured dry eye patient data samples. The method includes the step of validating the ML model with the set of well-structured dry eye patient data samples. The method includes the step of providing the set of eye data of the patient to the trained and validated ML model. With the trained and validated ML model, the method includes the step of classifying the set of eye data of the patient as a dry eye category and a dry eye type.