Machine Learning IOL Parameter Selection for Anomalous Eye Anatomy
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Solution Overview
Problem
Existing methods for determining intraocular lens (IOL) parameters during cataract surgery are inadequate for patients with anomalous anatomical measurements, leading to suboptimal surgical outcomes.
Innovation Solution
Utilizing machine learning models trained on historical patient data to generate IOL parameters based on anatomical measurements, including optimal and contraindicated recommendations for cataract surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to determine IOL parameters based on anatomical measurements, then the process is straightforward for patients with normal measurements, but the accuracy and reliability of surgical outcomes deteriorate for patients with anomalous anatomical measurements
Solution Approach 1:
The patent transforms the approach from using fixed formula-based calculations to dynamic machine learning models that can adapt parameters based on the specific characteristics of each patient's anatomical measurements. The ML models learn from historical data to determine optimal IOL parameters tailored to each patient's unique anatomy, including anomalous cases.
Solution Approach 2:
The patent creates a digital twin or virtual representation of the patient's eye by training machine learning models on historical patient data. This digital model allows surgeons to simulate and predict surgical outcomes for different IOL parameter combinations before actual surgery, enabling optimized decision-making for anomalous anatomies.
2Measurement precision
If machine learning models are implemented to determine IOL parameters, then the accuracy for anomalous cases improves, but the system complexity increases
Solution Approach 1:
The patent performs preliminary training of machine learning models using extensive historical patient data before actual surgical applications. This pre-computation phase creates ready-to-use models that can quickly provide accurate recommendations during surgery planning, separating the complex training process from the actual clinical decision-making process.
Solution Approach 2:
The patent introduces machine learning models as an intermediary layer between raw anatomical measurements and IOL parameter selection. These models process the complex relationships between multiple anatomical parameters and translate them into optimized IOL recommendations, shielding surgeons from the underlying computational complexity while providing enhanced accuracy.
3Reliability
If comprehensive historical data is utilized to train machine learning models, then the quality of IOL parameter recommendations improves, but the data processing requirements and computational resources increase
Solution Approach 1:
The patent extracts and utilizes only the most relevant features and patterns from comprehensive historical patient data during model training. By focusing on key anatomical parameters and their relationships with successful surgical outcomes, the system leverages essential information without being overwhelmed by the full volume of raw data.
Solution Approach 2:
The patent employs machine learning models that can process and learn from large volumes of historical data more efficiently than traditional methods. The models are trained on extensive datasets to capture rare anomalous cases and complex parameter relationships, achieving high recommendation quality by utilizing sufficient data volume rather than attempting to process all possible data comprehensively.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for performing surgical ophthalmic procedures, such as cataract surgeries. An example method generally includes generating, using one or more measurement devices, one or more data points associated with measurements of one or more anatomical parameters for an eye to be treated. Using one or more trained machine learning models, one or more recommendations are generated including one or more IOL parameters for the IOL to be used in the cataract surgery based, at least in part, on the one or more data points. The machine learning models are trained based on at least one historical data set of data points associated with measurements of anatomical parameters mapped to treatment data and treatment result data associated with each historical patient. The one or more IOL parameters comprise one or more of an IOL type, an IOL power, or IOL placement information for implanting the IOL in the eye.