ML Scanning Parameter Determination for Clinical Imaging Workflows
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Solution Overview
Problem
The determination of scanning parameters for medical devices, such as MRI systems, is time-consuming, labor-intensive, and inefficient, often requiring manual input by scanning technicians based on clinician requirements, leading to potential inefficiencies and prolonged exposure to radiation if results are not satisfactory.
Innovation Solution
A system and method utilizing a scanning parameter determination model, trained on clinical information, to automatically determine and update scanning parameters, including symptom information, and detect and process potential abnormalities during scans, reducing labor and improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual determination of scanning parameters is used, then flexibility and adaptability to specific clinical requirements are maintained, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system enables self-service by allowing the scanning system to automatically determine optimal scanning parameters based on clinical information input by clinicians, eliminating the need for manual scheduling by technicians and reducing time consumption while maintaining operational flexibility
Solution Approach 2:
The patent replaces the manual mechanical process of parameter determination with an automated intelligent system that uses machine learning models to determine scanning parameters, thereby reducing labor intensity and time consumption while maintaining adaptability to clinical requirements
2Reliability
If multiple scanning procedures are scheduled manually to satisfy clinician requirements, then comprehensive diagnostic coverage is achieved, but labor intensity and time consumption increase
Solution Approach 1:
The system provides multi-functionality by integrating multiple scanning procedure determination capabilities into a single automated system that can handle various diagnostic requirements simultaneously, reducing the complexity of scheduling multiple procedures while maintaining comprehensive diagnostic coverage
Solution Approach 2:
The patent introduces an intelligent intermediary system that acts as a mediator between clinician requirements and scanning execution, automatically translating clinical needs into optimized scanning parameter schedules, thereby reducing the complexity of manual scheduling while ensuring comprehensive diagnostic coverage
3Device complexity
If scanning parameters are determined without automated assistance, then system simplicity is maintained, but scanning efficiency and productivity decrease
Solution Approach 1:
The patent replaces simple manual parameter determination with an automated intelligent system using machine learning models, increasing scanning efficiency and productivity while managing system complexity through modular architecture and automated operations
Data Source
AI summary
The present disclosure provides systems, devices, and methods for scanning parameter determination. The systems may display, via a user terminal, a user interface including one or more input items for a user to input clinical information relating to a target subject. The clinical information may at least include symptom information. The systems may receive, from the user terminal, the clinical information relating to the target subject. The systems may further determine, based on the clinical information using a scanning parameter determination model, recommended values of scanning parameters to be used in a scan of the target subject. The scanning parameter determination model may be a trained machine learning model.