Deep Learning Medical System for Automated Image Quality Assessment
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Solution Overview
Problem
Healthcare facilities face challenges in providing quality care due to economic, technological, and administrative hurdles, including limited access to imaging systems, staff shortages, and the need for improved image quality metrics for accurate diagnosis.
Innovation Solution
A deep learning medical system and method for automatically generating image quality metrics using a deployed learning network model trained with labeled reference medical images, which processes medical images to determine their quality and outputs a metric for improved diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image quality assessment is performed by healthcare providers, then diagnostic accuracy can be maintained, but the workload and time consumption increase significantly
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated deep learning neural network system. The neural network model automatically evaluates medical images for quality metrics such as noise, artifacts, and diagnostic adequacy, substituting the human provider's manual review with an automated computational system that maintains accuracy while reducing time and workload.
Solution Approach 2:
The system enables self-service by allowing the imaging system to automatically assess its own output quality without requiring external human intervention. The deep learning model is integrated into the workflow to autonomously evaluate images, generate quality scores, and provide feedback, making the quality assessment process self-sufficient and reducing dependency on human reviewers.
2Reliability
If more imaging systems and staff are deployed to improve care quality, then patient care quality improves, but operational costs and system complexity increase
Solution Approach 1:
The deep learning quality assessment system serves multiple functions within the healthcare ecosystem: it evaluates image quality, provides diagnostic support, optimizes imaging protocols, and reduces the need for additional specialized staff. This multi-functional approach allows a single system to address multiple care quality needs without proportionally increasing system complexity or resource requirements.
3Productivity
If automated image processing is implemented to reduce provider burden, then workflow efficiency improves, but measurement precision of quality assessment may deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the deep learning model's quality assessments are continuously refined based on ground truth data and expert validation. The neural network learns from labeled training data with known quality outcomes and adjusts its evaluation criteria accordingly, ensuring that automated assessments maintain high precision while improving workflow efficiency. The feedback loop allows the system to learn and improve over time.
Data Source
AI summary
Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.