Machine Learning Ultrasound Object Detection for Diagnostic Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The overuse and misuse of advanced medical imaging techniques like CT, MRI, and PET for musculoskeletal disorders lead to scheduling delays, additional costs, and unnecessary radiation exposure, prompting the need for alternative imaging methods such as ultrasound that are more accessible and safer for patients.
Innovation Solution
Utilizing machine learning models trained on ultrasound images to identify anatomical features, disruptive features, and instruments, providing real-time diagnostic and interventional insights to facilitate clinical diagnoses and interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced medical imaging techniques (CT, MRI, PET) are used for musculoskeletal disorders, then diagnostic accuracy is improved, but patient exposure to radiation and scheduling delays increase
Solution Approach 1:
The patent uses machine learning models trained on ultrasound images to create a virtual copy or representation of the diagnostic capabilities previously requiring CT, MRI, or PET. The AI model processes ultrasound data to generate diagnostic information that replicates the accuracy of advanced imaging without requiring those modalities, thereby eliminating radiation exposure while maintaining diagnostic precision
Solution Approach 2:
The patent replaces the physical mechanical imaging systems (CT scanners, MRI machines, PET scanners) with an intelligent software-based system. Instead of using complex mechanical imaging equipment that exposes patients to radiation, the system uses machine learning algorithms processing ultrasound images to achieve equivalent or superior diagnostic accuracy, substituting computational intelligence for mechanical imaging hardware
2Measurement precision
If advanced medical imaging techniques (CT, MRI, PET) are used for musculoskeletal disorders, then diagnostic accuracy is improved, but scheduling delays and costs increase
Solution Approach 1:
The machine learning model creates a virtual diagnostic system that processes ultrasound images immediately, replicating the diagnostic accuracy of scheduled advanced imaging studies without requiring patients to wait for availability of CT, MRI, or PET scanners. The AI provides instant diagnostic insights that would otherwise require lengthy scheduling processes
Solution Approach 2:
The system enables the ultrasound imaging system itself to perform advanced diagnostic analysis through integrated machine learning capabilities. Rather than requiring separate scheduling of advanced imaging studies, the ultrasound system automatically processes images through AI algorithms to provide immediate diagnostic results, making the imaging system self-sufficient for comprehensive musculoskeletal evaluation
3Productivity
If machine learning models are used to process ultrasound images, then diagnostic efficiency is improved, but computational complexity increases
Solution Approach 1:
The machine learning models are trained in advance on large datasets of ultrasound images with corresponding diagnostic outcomes. This preliminary training phase prepares the models to quickly and efficiently process new ultrasound images during actual diagnostic sessions. The heavy computational work of learning patterns is performed beforehand, allowing rapid inference during clinical use without real-time computational burden
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the ultrasound imaging system and the diagnostic interpretation process. These models act as intelligent mediators that automatically analyze ultrasound images, extract relevant features, and generate diagnostic insights, bridging the gap between raw imaging data and clinical decision-making while reducing the need for manual analysis and improving overall diagnostic efficiency
Data Source
AI summary
Systems and methods are disclosed herein for processing ultrasound images to identify objects for diagnostic and/or interventional use. For instance, an ultrasound image of an anatomical structure may be received from a computing device of an ultrasound imaging system. The ultrasound image may be input to a machine learning model that is trained to identify a plurality of objects in ultrasound images of the anatomical structure. The plurality of objects may include anatomical features, disruptive features, and/or instruments. A prediction of one or more objects from the plurality of objects identified in the ultrasound image may be received as output of the machine learning model. An indication of the prediction may be provided to the computing device for display on a display of the ultrasound imaging system.