AI Screening Algorithm for Oropharyngeal Dysphagia Risk Stratification
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Solution Overview
Problem
Current diagnosis methods for oropharyngeal dysphagia are inefficient, leading to high rates of false positives and false negatives, resulting in resource wastage and inadequate detection of affected patients, due to a lack of awareness and inadequate screening processes among healthcare professionals.
Innovation Solution
An optimized screening system utilizing an AI-driven algorithm that filters patients based on clinical records and predictive models, such as random forests and Bayesian networks, to determine the risk of oropharyngeal dysphagia, ensuring only high-risk patients proceed to further diagnostic phases, thereby improving sensitivity and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional screening methods are used, then the diagnostic process is simple and easy to perform, but the detection precision is low leading to high false positive and false negative rates
Solution Approach 1:
The patent introduces an AI algorithm as an intermediary between traditional screening methods and final diagnosis. The algorithm processes clinical records and screening results to generate risk predictions, acting as a mediator that enhances detection precision without requiring complete redesign of the screening workflow. This resolves the contradiction by adding computational intelligence while maintaining operational simplicity.
Solution Approach 2:
The patent replaces manual clinical judgment and simple filtering methods with an AI-driven predictive model. The mechanical process of human assessment is substituted with automated machine learning algorithms (random forests, Bayesian networks) that objectively analyze clinical data, thereby improving measurement precision while the system manages complexity through automation.
2Reliability
If all patients undergo clinical exploration and instrumental evaluation, then the detection sensitivity is high, but the resource consumption is excessive
Solution Approach 1:
The patent segments the patient population into high-risk and low-risk groups using the AI algorithm's risk prediction. Only high-risk patients are directed to undergo expensive clinical exploration and instrumental evaluation, while low-risk patients receive simpler management. This segmentation maintains high detection sensitivity for true cases while dramatically reducing resource consumption by avoiding unnecessary testing of low-risk individuals.
Solution Approach 2:
The patent performs preliminary risk stratification using the AI algorithm before committing patients to resource-intensive diagnostic pathways. This preliminary action filters the patient population in advance, ensuring that only those most likely to benefit from extensive testing proceed, thereby optimizing the balance between detection sensitivity and resource utilization.
3Ease of operation
If simple screening methods are used, then the ease of operation is high, but the false positive rate is high leading to unnecessary follow-up tests
Solution Approach 1:
The AI algorithm performs self-service by automatically analyzing clinical records and generating risk predictions without requiring complex manual intervention. The system independently processes data, applies predictive models, and outputs risk stratification, maintaining ease of operation while improving precision. This resolves the contradiction by automating the analytical process rather than requiring complex human judgment protocols.
4Measurement precision
If traditional three-phase diagnosis is used, then the comprehensive evaluation is thorough, but the time consumption is high
Solution Approach 1:
The patent implements preliminary risk stratification using the AI algorithm at the beginning of the diagnostic process. This preliminary action enables immediate identification of high-risk patients who need comprehensive three-phase evaluation versus low-risk patients who can be managed with simpler approaches. The time loss is minimized because the stratification occurs automatically and rapidly, allowing appropriate diagnostic pathways to be initiated without delay.
Solution Approach 2:
The patent creates a dynamic diagnostic pathway where the depth of evaluation is adjusted based on AI-generated risk predictions. High-risk patients receive the full three-phase comprehensive evaluation, while low-risk patients receive streamlined assessment. This dynamic adaptation maintains high diagnostic accuracy for those who need it while reducing time consumption for those who don't, resolving the contradiction between thoroughness and efficiency.
Data Source
AI summary
Different aspects of the invention implement a system, and corresponding method, for the systematic, universal and optimized screening of oropharyngeal dysphagia which is based on an algorithm which takes into account parameters and clinical record of each patient, for determining with high probability the possibilities of suffering from oropharyngeal dysphagia, and selecting only those patients which really have a risk of suffering from oropharyngeal dysphagia for the continuation of their medical diagnosis and clinical exploration and instrumental assessment phases.