Organ-Specific CT Scan Classification for Abdominal Pain Diagnosis
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Solution Overview
Problem
Emergency department physicians often struggle to accurately diagnose the source of abdominal pain due to lack of domain knowledge and time constraints, leading to potential miss-diagnosis or undiagnosis, even with computed tomography (CT) scans.
Innovation Solution
The implementation of a system that partitions CT scan data by organ and region, using separate machine-learnt classifiers for each to determine the likelihood of abdominal pain causes, providing physicians with guided diagnosis through a graphic user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine-learnt classifiers are used to analyze each organ separately, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the abdominal region into multiple organs (liver, spleen, kidneys, pancreas, gallbladder, intestines, stomach, aorta) and applies separate machine-learnt classifiers to each organ. This segmentation allows each classifier to specialize in detecting specific pathologies within its assigned organ, improving diagnostic accuracy while managing complexity through modular organization.
Solution Approach 2:
The system employs a universal framework that processes CT scan data through multiple organ-specific classifiers simultaneously. Each classifier is trained on organ-specific data but follows a common architectural pattern, allowing the system to handle diverse abdominal pathologies through a unified multi-functional approach.
2Reliability
If comprehensive organ-by-organ analysis is performed, then diagnostic completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary segmentation of the abdominal CT scan into distinct organ regions before applying classifiers. This pre-processing step organizes the data in advance, allowing parallel processing of multiple organ classifiers simultaneously, which maintains comprehensive analysis while reducing overall processing time.
Solution Approach 2:
The patent implements continuous processing where multiple organ classifiers operate in parallel on segmented organ data. This parallel execution maintains the completeness of organ-by-organ analysis while significantly reducing the sequential processing time that would result from analyzing each organ one after another.
3Productivity
If automated classification is implemented, then productivity is improved, but ease of operation decreases
Solution Approach 1:
The system provides feedback to the physician by presenting organized diagnostic results that include the most likely causes of abdominal pain ranked by probability, along with the specific organs involved. This feedback mechanism translates complex classifier outputs into clinically actionable information, maintaining high diagnostic speed while preserving ease of interpretation for medical professionals.
Data Source
AI summary
To assist a physician in diagnosis of trauma involving abdominal pain, scan data representing the patient is partitioned by organ and/or region. Separate machine-learnt classifiers are provided for each organ and/or region. The classifiers are trained to indicate a likelihood of cause of the pain. By outputting results from the collection of organ and/or regions specific classifiers, the likeliest causes and associated organs and/or regions may be used by the physician to speed, confirm, or guide diagnosis of the source of abdominal pain.