Explainable AI Mental Disorder Diagnosis Protocol
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Solution Overview
Problem
Diagnosing mental disorders, such as autism spectrum disorder, is challenging due to complex psychiatric symptoms and insufficient neurobiological evidence, making it difficult to draw accurate conclusions and present supporting medical evidence in a time-efficient manner, especially with low reliability in high-complexity diagnosis using typical modeling methods.
Innovation Solution
A computer system utilizing an artificial neural network to determine test regions and processes for mental disorder diagnosis in brain images, providing a protocol that guides diagnosis and predicts the presence and severity of mental disorders, thereby improving diagnostic accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If typical modeling methods are used for high-complexity mental disorder diagnosis, then the diagnosis process can be simplified, but the reliability for drawing conclusions is not high due to black-box characteristics
Solution Approach 1:
The patent introduces an explainable AI intermediary layer that mediates between the complex neural network model and the final diagnosis. This intermediary provides explanation data that reveals the reasoning process, making the black-box model transparent while maintaining its diagnostic accuracy and complexity efficiency.
Solution Approach 2:
The system implements feedback by generating explanation data that returns information about the diagnostic reasoning process. This feedback loop allows clinicians to understand how conclusions were reached, enabling verification and adjustment of the diagnosis while maintaining the efficiency of automated analysis.
2Measurement precision
If comprehensive medical evidence is collected for accurate diagnosis, then diagnostic accuracy is improved, but the time required for diagnosis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing medical evidence before the actual diagnosis. The neural network model pre-analyzes multiple pieces of evidence and prepares structured explanation data in advance, so that when diagnosis is needed, the comprehensive analysis is already completed and ready for rapid presentation.
Solution Approach 2:
The patent creates a digital copy of the diagnostic reasoning process through explanation data. Instead of requiring clinicians to manually trace through all evidence and reasoning steps, the system generates a simplified copy that captures the essential diagnostic logic, maintaining accuracy while dramatically reducing the time needed to review comprehensive evidence.
3Loss of information
If multiple pieces of medical evidence are analyzed to support diagnosis, then the completeness of medical evidence is improved, but the difficulty of presenting evidence in time-efficient order increases
Solution Approach 1:
The patent segments the comprehensive medical evidence into structured components with hierarchical relationships. The explanation data divides evidence into relevant features, their importance weights, and logical connections, allowing complete evidence analysis to be presented in an organized, time-efficient manner rather than as an overwhelming collection of discrete data points.
Data Source
AI summary
Provided are a computer system for automatically searching for a mental disorder diagnosis protocol and an method thereof that may determine at least one test region to be examined for a predetermined mental disorder diagnosis in a brain image of a patient based on a first artificial neural network, may determine a test process for the mental disorder diagnosis for the patient based on a second artificial neural network, and may provide a test protocol for the mental disorder diagnosis for the patient based on the test region and the test process. The computer system may visualize at least one of a position, a shape, a size, and an importance of the test region in the brain image. The test process may include test order of a plurality of test stages in which the brain image is to be used for the mental disorder diagnosis.