Venous Access Imaging and AI Scoring for Difficult Cannulation
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Solution Overview
Problem
Nurses face challenges in placing peripheral intravenous lines due to difficult venous access (DVA) in adults and children, leading to increased patient anxiety, delayed treatment, and higher costs, with no assessment scale or guidelines to predict or manage this difficulty.
Innovation Solution
A medical system utilizing a vascular assessment device and machine learning model to analyze raw image data from ultrasound, infrared, or optical coherence tomography to determine a DVA assessment, providing a score and suggested catheter size, angle, and insertion device, and integrating with automated catheter insertion systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple venipuncture attempts are made to achieve venous access, then catheter insertion success is eventually achieved, but patient anxiety and suffering increase, treatment is delayed, and costs increase
Solution Approach 1:
The system performs preliminary assessment of venous accessibility using imaging technology (ultrasound, infrared, or optical coherence tomography) and machine learning analysis before the actual catheter insertion attempt. This preliminary action identifies patients likely to experience difficult venous access, allowing clinicians to prepare appropriate techniques and equipment in advance, thereby reducing the number of failed attempts and treatment delays.
2Reliability
If multiple venipuncture attempts are made, then catheter insertion success is achieved, but the incidence of complications such as extravasation, vascular perforation, hematoma, and phlebitis increases
Solution Approach 1:
The system performs preliminary assessment of venous accessibility using imaging technology (ultrasound, infrared, or optical coherence tomography) and machine learning analysis before the actual catheter insertion attempt. This preliminary action identifies patients likely to experience difficult venous access, allowing clinicians to prepare appropriate techniques and equipment in advance, thereby reducing the number of failed attempts and treatment delays.
3Ease of operation
If no assessment scale is used, then the clinical workflow remains simple, but the ability to predict and manage difficult venous access is lacking
Solution Approach 1:
The system replaces manual clinical assessment with an automated machine learning model that processes imaging data to predict difficult venous access. The machine learning model analyzes imaging features and patient characteristics to generate a DVA assessment, eliminating the need for complex manual evaluation while providing objective, data-driven predictions.
4Measurement precision
If a complex assessment system with multiple imaging modalities and machine learning is implemented, then accurate DVA prediction is achieved, but device complexity increases
Solution Approach 1:
The system employs a multi-functional platform that can operate with different imaging modalities (ultrasound, infrared, or optical coherence tomography) depending on availability and clinical context. The machine learning model is trained to extract relevant features from various imaging types, allowing the system to maintain high predictive accuracy while adapting to different clinical settings and reducing the need for multiple specialized devices.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances cannulation success rates by allowing early identification of DVA, reducing emotional and financial burdens through improved venous access techniques.
Implementation Method 1
The vascular assessment device utilizes one or more of ultrasound imaging, infrared imaging, molecular imaging, Raman spectroscopy, or optical coherence tomography to acquire the raw image data
Implementation Method 2
The vascular assessment device utilizes one or more of ultrasound imaging, infrared imaging, molecular imaging, Raman spectroscopy, or optical coherence tomography to acquire the raw image data
Implementation Method 3
The vascular assessment device utilizes one or more of ultrasound imaging, infrared imaging, molecular imaging, Raman spectroscopy, or optical coherence tomography to acquire the raw image data
Data Source
AI summary
A system and method for determining a difficult venous access of a patient. Logic processes meta data acquired by a vasculature assessment device coupled with the patient. The meta data, e.g., vessel diameter, vessel depth, vessel wall thickness, vessel wall elasticity, tissue elasticity, tissue profusion, blood flow rate, or hydration level. The logic further determines a difficult venous access by performing an algorithm on the meta data. The algorithm is defined utilizing machine learning techniques applied to an ongoing collection data sets acquired from a plurality of systems across a plurality of patients undergoing catheter insertion events. The data set may also include patient data such as weight, age, etc. The vasculature assessment device may include ultrasound imaging, infrared imaging, molecular imaging, Raman spectroscopy, or optical coherence tomography to acquire one or both of three-dimensional imaging data and the meta data.