Granted Patent B2

US 12,373,948 · App. 18/289,856 · Granted Jul 29, 2025

Inventors: Chester Edlund (San Antonio, TX); Brian Lawrence (San Antonio, TX); Christopher J. Sandroussi (San Antonio, TX); James Laird (San Antonio, TX)

Assignee: KCI Manufacturing Unlimited Company

G06T7/0012A61B5/0077A61B5/7455A61B5/7475G06T3/40G06T7/11G06T7/13G06T7/136G06T7/50G06T7/62G06V10/771G06V10/82H04N23/631H04N23/635H04N23/64G06T2207/10028G06T2207/10048G06T2207/20084G06T2207/30088G06V2201/03

Abstract

Claims (89)

1. A system for measuring a wound site, comprising:

an image capture device;

a touchscreen;

a vibration motor; and

a processor;

wherein the image capture device is configured to:

capture a digital image, and

capture a depth map associated with the digital image;

wherein the processor is configured to:

determine a bounding box of the wound site by processing the digital image with a first trained neural network,

determine a wound mask by processing the digital image with a second trained neural network,

determine a wound boundary from the wound mask,

determine whether the wound boundary is aligned within a camera frame, and

generate a wound map from the depth map, wound mask, and wound boundary;

wherein the processor and vibration motor are configured to:

provide a series of vibrations at the vibration motor in response to the processor determining that the wound is aligned within the camera frame.

2. The system of claim 1 , wherein the processor is further configured to:

scale the digital image to generate a scaled image;

pad the scaled image to generate a padded image;

determine a plurality of possible wound regions by inputting the padded image into the first trained neural network; and

determine the bounding box by applying a non-maximum suppression algorithm to the plurality of possible wound regions.

3. The system of claim 2 , wherein the processor is further configured to:

select a selected possible wound region from the plurality of possible wound regions,

wherein the selected possible wound region has a highest objectiveness score within the plurality of possible wound regions;

move the selected possible wound region to a set of probable wound regions;

calculate an Intersection over Union of the selected possible wound region with each possible wound region in the plurality of possible wound regions; and

remove each possible wound region in the plurality of possible wound regions having an Intersection over Union with the selected possible wound region lower than a threshold.

4. The system of claim 1 , wherein the processor is further configured to:

scale the digital image to generate a scaled image;

normalize pixel values of the scaled image to generate a normalized image;

determine a raw mask by inputting the normalized image into the second trained neural network; and

transform the raw mask into a wound mask.

5. The system of claim 4 , wherein the processor is further configured to:

select a pixel value threshold;

set values of raw mask pixels greater than the pixel value threshold to 1; and

set values of raw mask pixels not greater than the pixel value threshold to 0.

6. The system of claim 5 , wherein the processor is further configured to:

set values of raw mask pixels having a value of 1 to 255; and

output the raw mask as the wound mask.

7. The system of claim 1 , wherein the processor is further configured to:

generate a wound surface mask by interpolating depth information of portions of the depth map outside of the wound boundary;

generate wound depth data by calculating a depth difference between the wound surface map and the wound map;

calculate a mathematical length of the wound and a mathematical width of the wound from the wound map; and

calculate a wound mathematical volume from the mathematical length of the wound, the mathematical width of the wound, and depth data of the wound map.

8. The system of claim 7 , wherein the processor is further configured to:

assign an orientation axis to the wound map;

calculate a standard length of the wound and a standard width of the wound from the wound map and the orientation axis;

determine a standard depth of the wound from the wound map; and

calculate a standard wound volume from the standard length of the wound, the standard width of the wound, and the standard depth of the wound.

9. The system of claim 1 , wherein the series of vibrations comprises two quick vibrations with ascending intensity.

10. The system of claim 1 , wherein the processor and touchscreen are configured to:

display a visual indicator on the touchscreen in response to the processor determining that the wound is aligned within the camera frame.

11. The system of claim 1 , further comprising:

a speaker assembly;

wherein the processor and speaker assembly are configured to:

output an audible alert from the speaker assembly in response to the processor determining that the wound is aligned within the camera frame.

12. The system of claim 1 , further comprising:

a speaker assembly;

wherein the processor and speaker assembly are configured to:

output an audible alert from the speaker assembly in response to the processor determining that the wound is not aligned within the camera frame.

13. The system of claim 1 , wherein the image capture device comprises:

an electro-optical camera;

an infrared camera; and

a light emitting module.

14. The system of claim 13 , wherein the light emitting module is configured to emit multiple light rays according to a pattern.

15. The system of claim 1 , wherein the image capture device comprises:

an electro-optical camera; and

a time-of-flight camera.

16. A non-transitory computer-readable medium comprising executable instructions for performing steps in a method for measuring a wound site, wherein the executable instructions configure a processor:

receive a digital image;

receive a depth map associated with the digital image;

determine a bounding box of the wound site by processing the digital image with a first trained neural network;

determine a wound mask by processing the digital image with a second trained neural network;

determine a wound boundary from the wound mask;

determine whether the wound boundary is aligned within a camera frame; and

generate a wound map from the depth map, wound mask, and wound boundary.

17. The non-transitory computer-readable medium of claim 16 , wherein the executable instructions further configure the controller to:

output a signal to generate a first series of vibrations at a vibration motor in response to the processor determining that the wound is aligned within the camera frame.

18. A method for measuring a wound site, comprising:

capturing a digital image of the wound site with an image capture device;

capturing a depth map associated with the digital image with the image capture device;

determining a bounding box of the wound site by processing the digital image with a first trained neural network;

determining a wound mask of the wound site by processing the digital image with a second trained neural network;

determining a wound boundary from the wound mask;

generating a wound map from the depth map, wound mask, and wound boundary;

determining whether the wound boundary is aligned within a camera frame; and

providing a series of vibrations at a vibration motor if the wound is aligned within the camera frame.

19. The method of claim 18 , wherein the series of vibrations comprises two quick vibrations with ascending intensity.

20. The method of claim 18 , further comprising outputting an audible alert from a speaker assembly if the wound boundary is aligned within the camera frame.

Assignments (1)

Continuity (2)

References Cited (200)