Camera-Based Protocol Guidance for Medical Imaging Preparation
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Solution Overview
Problem
Medical imaging scanners face high scanning error rates due to the variety of imaging protocols and varying technician experience levels, leading to incorrect protocol selection and improper patient positioning, which can result in unusable images and compromised patient outcomes.
Innovation Solution
A system utilizing camera-based deep learning neural networks to infer the prescribed imaging protocol and patient preparation, providing real-time guidance through on-screen instructions or laser beams to ensure correct patient positioning and coil placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide variety of imaging protocols is offered to handle different clinical needs, then the system's adaptability and versatility improve, but the complexity of protocol selection and execution increases, leading to higher error rates
Solution Approach 1:
The system automatically infers the prescribed imaging protocol from the clinical indication without requiring manual selection by the technician. The deep learning model processes the clinical input and autonomously determines the appropriate protocol parameters, allowing the system to serve itself in the protocol selection task and reducing human error.
Solution Approach 2:
The manual protocol selection process is replaced with an automated deep learning-based inference system. Instead of relying on technician knowledge and manual configuration, the system uses machine learning models to automatically determine protocol parameters, substituting human cognitive processes with computational algorithms.
2Ease of operation
If manual protocol selection and patient positioning rely on technician experience, then the ease of operation is maintained, but the reliability of scanning outcomes deteriorates due to varying experience levels
Solution Approach 1:
The system provides real-time feedback to technicians during patient positioning by analyzing camera images and comparing them against the inferred protocol requirements. The system highlights positioning errors and guides corrections, creating a closed-loop feedback mechanism that improves reliability while maintaining ease of operation.
Solution Approach 2:
The system performs preliminary inference of the imaging protocol and generates expected positioning guidelines before the actual scanning process. This advance preparation allows the system to establish correct positioning criteria in advance, enabling technicians to follow pre-determined guidelines rather than relying on experience during the critical scanning moment.
3Reliability
If real-time camera-based monitoring and deep learning inference are implemented, then scanning reliability improves, but the device complexity and computational requirements increase
Solution Approach 1:
The complex monitoring task is divided into separate specialized modules: a protocol inference module that determines imaging parameters, a positioning analysis module that processes camera images, and a feedback generation module that provides guidance. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The deep learning models are designed to handle multiple functions: the protocol inference model processes clinical indications to determine imaging parameters, while the positioning model analyzes various types of camera images (patient positioning, coil placement). This multi-functionality reduces the need for separate specialized systems and lowers overall complexity.
Data Source
AI summary
Systems or techniques that facilitate camera-based deep learning prediction and guidance for medical imaging protocols are provided. In various embodiments, a system can infer, via execution of a first deep learning neural network, a prescribed imaging protocol that is to be performed by a medical imaging scanner on a medical patient. In various aspects, the system can infer, via execution of a second deep learning neural network on a preparation image or video of the medical patient that is captured by a camera associated with the medical imaging scanner, whether or not the medical patient is prepared for the prescribed imaging protocol. In various instances, the system can, in response to an inference that the medical patient is not prepared for the prescribed imaging protocol, initiate an electronic guidance action that explains or shows how to make the medical patient prepared for the prescribed imaging protocol.