Diagnostic System Iterative Question List for Medical Condition
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Solution Overview
Problem
Current diagnostic systems for medical imaging are inefficient in providing accurate and timely diagnoses, as they rely on general information and do not account for specific patient conditions, leading to complexities in radiology interpretation across different organs and imaging types, resulting in time-consuming and inaccurate results.
Innovation Solution
A method that presents users with a list of questions related to medical images, allowing them to input the status of observations, which updates a list of conditions with associated likelihoods, iteratively reducing the number of questions and focusing on the most relevant conditions, using a database of principal observations, conditions, and rules to guide the diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic systems use general disease databases, then they provide broad information coverage, but they fail to provide accurate tailored solutions for specific patient cases
Solution Approach 1:
The diagnostic system segments the complex diagnostic process into discrete steps: presenting a limited list of foundation observations, receiving user selections, generating filtered condition and rule sets, and iteratively updating likelihoods. This segmentation transforms an overwhelming comprehensive analysis into manageable sequential tasks, improving diagnostic accuracy while controlling system complexity.
Solution Approach 2:
The system performs preliminary action by pre-defining foundation observations, conditions, and rules in a database before the actual diagnostic process. This pre-processing organizes medical knowledge into structured formats that can be quickly queried and filtered during diagnosis, enabling accurate tailored solutions without requiring complex real-time processing.
2Reliability
If comprehensive rule sets are applied to all conditions, then diagnostic coverage is complete, but the number of steps required increases significantly
Solution Approach 1:
The system extracts only the relevant subset of conditions and rules based on user selections from foundation observations. Instead of applying all possible diagnostic rules to every case, the system dynamically filters and extracts only those rules connected to the selected observations, maintaining diagnostic completeness while dramatically reducing the number of evaluation steps required.
Solution Approach 2:
The diagnostic system dynamically adapts the set of conditions and rules presented to the user based on previous selections. As users select foundation observations, the system dynamically updates and refines the condition set, removing irrelevant conditions and focusing only on those connected to the selected observations. This dynamic adaptation reduces diagnostic steps while maintaining completeness.
3Loss of information
If all observations are presented to the user simultaneously, then complete information is provided, but the user interface becomes overwhelming and difficult to navigate
Solution Approach 1:
The system segments the complete information set into a manageable list of foundation observations presented to the user. Instead of overwhelming users with all possible observations simultaneously, only the most relevant foundation observations are presented in a concise list. This segmentation maintains information completeness while dramatically improving user interface usability.
Solution Approach 2:
The system performs preliminary filtering to identify and present only the foundation observations that are most relevant to the current diagnostic context. This pre-selection of observations, based on pre-defined relationships in the database, ensures that users are presented with complete but condensed information, making the interface easy to navigate while preserving all necessary diagnostic data.
4Measurement precision
If expert systems are built to provide accurate diagnoses, then diagnostic precision improves, but difficulties in building and maintaining them arise
Solution Approach 1:
The system uses preliminary action by pre-defining foundation observations, conditions, and rules in a structured database format during the development phase. This pre-organization of medical knowledge into standardized relationships simplifies the building process, as the system automatically generates the diagnostic logic from these pre-defined elements rather than requiring complex custom programming for each diagnostic scenario.
Solution Approach 2:
The diagnostic system employs a universal framework where foundation observations, conditions, and rules are defined in a general-purpose database structure that can handle multiple diagnostic scenarios. This universal approach allows the same system architecture to support various medical domains and conditions, reducing development complexity while maintaining high diagnostic precision across different applications.
Data Source
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AI summary
The present invention relates to a method for assisting a user in determining a medical condition in a subject from one or more medical images by presenting the user with a list of questions, LOQ, about the subject that are to be answered with an affirmative indication (Y), a negative indication (N) or an unknown indication (X) by input by the user. In response to receiving an answer to one question, the method presents the user with a ranked list of conditions, LOC, and also with an updated LOQ wherein at least the answered question has been removed i.e. the LOQ iteratively collapses or reduces in length. An earlier step of selecting a foundation observation, FO, about the subject reduces the initial LOQ and the possible conditions.