Contactless Patient Weight Estimation via Depth Imaging
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Solution Overview
Problem
Existing methods for estimating patient weight, such as questioning or visual estimation, are prone to errors, and using scales introduces complexity and is not feasible for all patients, while contactless systems like depth cameras have limitations in accuracy and integration into clinical workflows.
Innovation Solution
A computer-implemented method using optical and depth cameras to automatically select frames with no patient keypoints and minimal movement, calculating patient volume from depth data, and estimating weight based on volume and typical tissue density, seamlessly integrated into medical imaging workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used, then device complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical measurement with an automated system using imaging technology and computer processing. The weight estimation is achieved by capturing images of the patient on the examination table, processing these images to determine table position and patient dimensions, then calculating weight from these parameters - eliminating the need for direct mechanical weight measurement devices.
2Productivity
If manual measurement methods are used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically capturing images and processing them to extract patient dimensions and table position information. This preliminary processing of visual data enables rapid weight estimation without requiring time-consuming manual measurement procedures, thereby improving productivity.
Solution Approach 2:
The manual mechanical measurement process is replaced with an automated imaging and computer-based calculation system. The examination table's imaging capabilities and the computer's processing power work together to automatically determine patient weight from visual data, significantly improving measurement efficiency.
3Measurement precision
If automated imaging system is used, then measurement precision and productivity improve, but device complexity increases
Solution Approach 1:
The examination table serves multiple functions: it is both the patient support device and the imaging system. The table includes imaging means that can capture images during the examination process, eliminating the need for separate dedicated imaging equipment and reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The system uses the examination table's own imaging capabilities to perform the weight estimation function. The table captures images of the patient on it, and the computer processes these self-generated images to calculate weight, making the system self-sufficient and reducing the need for additional external devices.
4Measurement precision
If patient is moved for measurement, then measurement accuracy improves, but reliability deteriorates
Solution Approach 1:
The system performs preliminary image capture and processing while the patient remains in their original position on the examination table. By extracting dimensional information and determining table position from images taken in situ, the system eliminates the need to move the patient, thereby maintaining measurement reliability while achieving sufficient precision.
Solution Approach 2:
Instead of physically moving the patient to a separate measurement device, the system uses imaging technology to capture and analyze patient dimensions from the examination table position. This substitution of mechanical movement with optical measurement maintains patient stability and measurement reliability.
Data Source
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AI summary
A computer-implemented method is provided for estimating a weight of a patient when supported on a patient table. Optical image data and depth image data are obtained and patient body keypoints are extracted from the optical image data. A first frame is selected which comprises an image of the patient table, by selecting a frame in which no body keypoints are present in the optical image data. A second frame is selected of the patient on the table, by selecting a frame with patient body keypoints and with little or no movement. A patient volume is obtained based on a difference between the depth image data for the first and second frame (or depth data at those times) and the patient weight is estimated from the determined patient volume.