Image-Based Clinical Decision Support with AI-Guided Follow-Up Scans
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Solution Overview
Problem
Existing medical imaging systems require a sequence of scans based on previous results, leading to time delays, organizational burdens, and patient dissatisfaction due to the lack of consideration for clinical context.
Innovation Solution
Implementing a test-algorithm chain using AI and machine learning to automatically determine clinical findings and generate decision data for further imaging, including suggested parameter values, to optimize the imaging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated diagnosis is implemented to reduce time delays, then productivity is improved, but device complexity increases
Solution Approach 1:
A radiology information system serves as an intermediary between the imaging system and the automated diagnosis system, managing workflow coordination and data flow. This mediator handles the complexity of integrating multiple systems while keeping the core imaging and diagnosis functions relatively simple and focused.
Solution Approach 2:
The system is divided into separate functional modules: imaging acquisition, automated diagnosis processing, and result integration. Each module operates independently with well-defined interfaces, allowing the complexity to be distributed and managed separately rather than concentrated in a single complex system.
2Measurement precision
If follow-up scans are scheduled manually based on radiologist assessment, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
Automated diagnosis algorithms analyze imaging data immediately after acquisition, performing preliminary assessment before radiologist review. This preliminary action identifies suspicious findings and determines the need for follow-up scans in advance, enabling faster scheduling while maintaining diagnostic accuracy through subsequent radiologist verification.
3Reliability
If multiple imaging scans are performed in sequence, then reliability is improved, but loss of time increases
Solution Approach 1:
The automated diagnosis system provides immediate feedback on imaging results, analyzing data as it becomes available and notifying the radiology information system of findings requiring follow-up. This continuous feedback loop enables real-time decision-making about additional scans, optimizing the timing and necessity of sequential imaging to maintain reliability while minimizing delays.
Data Source
AI summary
Techniques are described for providing image-based operational decision support. The technique includes providing initial imaging data from a preceding examination of a patient, determining clinical findings by automated processing of the initial imaging data, generating decision data at least comprising a decision whether a further recording of a number of images is necessary, and generating a suggested set of imaging parameter values for recording this number of images. The decision data may be based on the determined clinical findings, and the technique may further include outputting the suggested set of imaging parameter values for recording a number of images of the patient. Also described are a related clinical decision system, a decision module, and a related medical imaging system.