AI Imaging Apparatus for Radiological Finding Alerts
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Solution Overview
Problem
Healthcare facilities face challenges in providing quality care due to economic, operational, and technological hurdles, including limited staff skills, equipment complexity, and the need for effective management of imaging and information systems, particularly in geographically distributed networks where direct access to data and collaboration are hindered.
Innovation Solution
The implementation of an imaging apparatus equipped with a processor that evaluates image quality and uses a trained learning network to generate analyses, identify clinical findings, and trigger notifications for healthcare practitioners, facilitating improved image processing and quality control at the point of care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image processing and analysis is performed by healthcare professionals, then diagnostic accuracy can be maintained through human expertise, but time consumption and resource intensity increase significantly
Solution Approach 1:
The patent introduces an AI-based image processing system as an intermediary between the imaging device and healthcare professionals. This intermediary automatically performs preliminary image analysis, quality assessment, and finding identification, thereby reducing the time healthcare professionals need to spend on manual processing while maintaining diagnostic accuracy through collaborative human-AI review.
Solution Approach 2:
The patent replaces manual mechanical image processing performed by healthcare professionals with an automated AI-based system. The AI system uses machine learning models to automatically analyze images, detect clinical findings, and generate reports, substituting the time-consuming manual process while maintaining or enhancing diagnostic capabilities.
2Reliability
If more healthcare staff are hired to improve image processing capabilities, then quality of care can be improved, but operational costs increase
Solution Approach 1:
The patent implements a self-service AI system that autonomously performs image quality assessment, finding detection, and report generation without requiring additional human staff. The system self-manages the image processing workflow, thereby maintaining or improving quality of care while eliminating the need for increased staffing resources.
Solution Approach 2:
The patent substitutes the mechanical process of human staff performing image analysis with an automated AI-based system. This substitution maintains diagnostic quality while significantly reducing the quantity of human resources required, as the AI system can process images continuously without fatigue or scheduling constraints.
3Measurement precision
If image analysis is performed manually, then comprehensive review can be ensured through human expertise, but productivity decreases
Solution Approach 1:
The patent introduces an AI intermediary that performs comprehensive preliminary review of images automatically. This intermediary system checks image quality, identifies clinical findings, and prioritizes cases requiring human review, thereby maintaining comprehensive review quality while significantly increasing overall productivity by handling routine cases autonomously.
Solution Approach 2:
The patent replaces manual comprehensive review with an automated AI-based review system that processes images at high speed. The AI system performs detailed analysis of image quality and clinical findings, maintaining thoroughness while achieving much higher throughput compared to manual processing by individual healthcare professionals.
4Reliability
If manual image quality evaluation is performed, then quality control can be maintained through professional judgment, but the complexity of equipment and processes increases
Solution Approach 1:
The patent replaces manual quality evaluation based on professional judgment with an automated AI-based quality control system. The AI system uses machine learning models trained on quality criteria to automatically assess image quality, thereby maintaining reliable quality control while reducing the complexity of manual processes and equipment management.
Solution Approach 2:
The patent implements a self-service quality control system that automatically evaluates image quality without requiring manual intervention. The system self-assesses quality parameters, identifies issues, and provides feedback, thereby maintaining reliable quality control while simplifying the overall system operation and reducing the complexity burden on healthcare professionals.
Data Source
AI summary
Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings, and generation of notification at or near a point of care are disclosed and described. An example imaging apparatus includes a processor to at least: process the first image data using a trained learning network to generate a first analysis of the first image data; identify a clinical finding in the first image data based on the first analysis; compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and, when comparing identifies a change between the first analysis and the second analysis, generate a notification at the imaging apparatus regarding the clinical finding to trigger a responsive action.