Learning-Based Medical Scanner Autonomous Operation
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Solution Overview
Problem
Existing medical scanners require a technician for high-quality image acquisition, leading to variable patient experience and image quality across facilities due to operator interaction and experience.
Innovation Solution
Training learning-based medical scanners to operate independently using deep reinforcement learning, enabling them to adapt to patient interactions, optimize scanning processes, and navigate around obstacles, while learning from technician interactions and patient feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a technician operates the scanner, then high-quality medical images are obtained, but patient experience and image quality vary across facilities due to operator interaction and experience
Solution Approach 1:
The scanner is equipped with autonomous capabilities to operate without technician intervention. The system automatically positions the patient, adjusts scanning parameters, executes the scanning protocol, and processes images independently, eliminating variability introduced by different operators across facilities.
Solution Approach 2:
The scanner dynamically adjusts scanning parameters such as exposure time, tube current, and reconstruction algorithms based on real-time patient characteristics detected by sensors. This adaptive parameter optimization ensures consistent high-quality images regardless of facility or operator differences.
2Reliability
If a technician operates the scanner, then scanning guidelines are observed, but the process requires human intervention which reduces efficiency
Solution Approach 1:
The scanner autonomously monitors and enforces scanning guidelines through embedded decision-making algorithms. The system automatically determines appropriate scanning protocols, adjusts parameters within safe ranges, and ensures regulatory compliance without requiring continuous human oversight, thereby improving both reliability and efficiency.
Solution Approach 2:
The system continuously monitors scanning parameters and patient responses, providing real-time feedback to automatically adjust the scanning process. This closed-loop control ensures guideline compliance while optimizing scan speed and efficiency based on actual conditions.
3Productivity
If the scanner operates independently, then efficiency and standardized protocols are improved, but the scanner must learn and adapt to individual patient needs
Solution Approach 1:
The patent replaces the mechanical system of human technician decision-making with an intelligent software system based on deep reinforcement learning. The scanner uses machine learning models to perceive patient needs, interpret medical requirements, and autonomously adjust scanning parameters, substituting cognitive functions with computational algorithms.
Solution Approach 2:
The scanner is pre-trained on extensive datasets of scanning scenarios, patient characteristics, and medical guidelines before deployment. This preliminary training enables the system to quickly adapt to individual patients during actual scanning without requiring complex real-time learning, thereby managing device complexity while maintaining high efficiency.
4Adaptability or versatility
If the scanner adapts to patient interactions over time, then patient-centric care is improved, but the adaptation process requires observing and learning from technician interactions
Solution Approach 1:
The scanner undergoes comprehensive pre-training on diverse patient data and interaction scenarios before clinical deployment. This advance preparation includes learning from simulated technician-patient interactions and real patient feedback, enabling the system to immediately provide personalized care without requiring extended adaptation time at each facility.
Solution Approach 2:
The system continuously learns from actual patient feedback and outcomes, incrementally improving its interaction strategies. By processing feedback in real-time and updating its models continuously, the scanner adapts to individual patient preferences efficiently without requiring lengthy dedicated training periods.
Data Source
AI summary
A method for training a learning-based medical scanner including (a) obtaining training data from demonstrations of scanning sequences, and (b) learning the medical scanner's control policies using deep reinforcement learning framework based on the training data.