Probabilistic Diagnosis System Using Bayesian Pattern Matching
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Solution Overview
Problem
Current medical image interpretation techniques are inefficient and limited by human observers, who are subjective, insensitive to subtle signal changes, and non-quantitative, leading to missed diagnoses and operational inefficiencies, while existing computer algorithms are slow and task-specific, preventing broad application in acute care settings.
Innovation Solution
A system and method for probabilistic diagnosis using medical imaging devices to extract key features from medical images, assigning quantitative or semi-quantitative values, and matching them to known disease-specific patterns using a processor, such as a Bayesian network, to provide efficient and accurate diagnostic support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human observers interpret medical images, then diagnostic experience and pattern recognition are applied, but subjectivity, insensitivity to subtle changes, and non-quantitative assessment lead to missed diagnoses
Solution Approach 1:
The patent introduces computational algorithms as an intermediary between the medical image and the human radiologist. These algorithms quantitatively analyze image features and provide objective measurements that supplement human interpretation, reducing subjectivity while preserving clinical expertise.
Solution Approach 2:
The patent replaces the purely human visual assessment system with a hybrid system that incorporates computational image analysis. This substitution introduces quantitative, invariable measurements that are insensitive to subtle signal changes, complementing human pattern recognition capabilities.
2Measurement precision
If computer algorithms are used for image analysis, then invariable and quantitative results are obtained, but the algorithms work slowly and have limited scope for narrowly defined tasks
Solution Approach 1:
The patent segments the image analysis task into distinct components: automated detection of specific features, quantitative measurement of those features, and integration with clinical decision-making. This allows different algorithmic approaches to be applied to different segments, improving overall efficiency.
Solution Approach 2:
The patent applies computational analysis selectively to key features rather than performing exhaustive analysis of all image data. This partial action approach maintains quantitative precision for critical measurements while reducing overall processing time to meet clinical deadlines.
3Reliability
If double reading by two human observers is performed, then missed diagnoses are decreased, but logistical demands and cost increase significantly
Solution Approach 1:
The patent creates a computational 'copy' or surrogate for the second reader's analysis. Instead of requiring a second human observer, the system uses algorithms to generate independent quantitative assessments that can be integrated with the primary radiologist's interpretation, achieving the benefits of double reading without the logistical burden.
4Ease of operation
If traditional report generation processes are used, then radiologists can create diagnostic reports, but the administrative components are tedious and consume 25% of interpretation time
Solution Approach 1:
The patent enables the system to automatically generate draft reports by integrating computational analysis results with clinical decision support. This self-service approach handles administrative components automatically, freeing radiologists from tedious documentation tasks while maintaining professional oversight.
Data Source
AI summary
Methods and systems for obtaining a probabilistic diagnosis based on medical images of a patient are disclosed. In exemplary embodiments, such methods include the steps of scanning some or all of a patient to obtain a medical image; evaluating the medical image for one or more designated key features; assigning discrete values to the one or more designated key features to form a patient scan key feature pattern; and transmitting the values of the one or more designated key features to a processor programmed to match the patient scan key feature pattern to one or more known disease-specific key feature patterns to create a probabilistic diagnosis and transmit the probabilistic diagnosis to a user. Associated systems include a medical imaging device that is capable of producing a medical image of some or all of a patient and a processor programmed to create a probabilistic diagnosis.