Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams

Pain is a subjective, multifaceted experience that can be difficult to assess using numerical rating scales or surveys with descriptors. Here we demonstrate how to quantitatively capture pain location and severity in a combined illustrative tool, the pain body diagram. The numerical rating scale, asking patients to rate pain from zero to 10, is the most commonly used pain rating tool.

The visual analog scale, rating pain from zero to 100, was designed to minimize anchoring bias, which can skew reported values despite varying pain levels. Additionally, the McGill Pain Questionnaire helps understand pain's somatosensory, and emotional aspects through descriptive prompts. Pain body diagrams have been used as a pain assessment tool to track pain symptoms longitudinally.

Sex-specific diagrams allow for respondents to better identify and report pain. In addition, the inclusion of color to signify intensity allows for effective communication of pain across cultural and language barriers. Pain is a ubiquitous yet complex experience.

While verbal descriptors and visual sliders are the current gold standard of pain metrics, they neglect the origin of pain on the layout of the body itself. Future PBD iterations could be expanded to represent the somatization of pain or visceral pain in a quantifiable method. Additionally, this tool could pave the way to understand pain treatments, such as our laboratory's larger focus on deep brain stimulation.

To begin, screen the patients using the displayed patient inclusion criteria. Then import a gender-appropriate PBD template showing both front and back body surfaces into an illustration application on a touch-sensitive digital tablet equipped with a pressure-sensitive drawing tool. After downloading the template to the tablet's photo library and importing it, create a new layer on top of the PBD template for the participant by clicking the layers icon and then the plus button.

This will result in two layers, one with the PBD and one for the participant's drawing with colors indicating pain. Next, create a new brush with X equals Y pressure to hue transformation curve by clicking the Brush Library icon and the plus button to open the Brush Studio. Click the color dynamics button and scroll to the color pressure section.

For the hue slider, click the numeric percentage to achieve a straight 45-degree line in the pressure transformation graph. To define the hue gradient range from green to blue to red, adjust the hue slider in the color pressure section by clicking the percentage number listed and entering a numeric value of 81%Adjust the pen tool slider to select a size suitable for the study participant's needs. Explain the pain body diagram or PBD anatomy and orientation of body templates in landscape mode.

Describe the pressure to hue linear transformation by showing that increased stylus pressure shifts hues from green to blue to red, representing mild, moderate, and severe pain intensity respectively. Then explain the drawing and erasing tools and pinch to zoom and panning functionalities. Confirm the participant's understanding of the pain body diagram task using the teach-back method and have them explain the process in their own words.

Allocate 15 minutes for participants to practice drawing pain body diagrams on a flat surface, ensuring an accurate representation of pain location, and intensity. Review the PBD with each participant to verify consistency and correct use of colors. Request participants to finish PBDs during baseline or at different time points following treatment or intervention, allowing ample time for PBD completion.

After completion, save PBDs with a standardized file name, including the patient ID, date, and time the PBD was finished. Select the desired images for bulk export using the select icon. Click on the Share button to access a list of available image formats.

Choose a portable or Photoshop file format that retains image layers and click on the preferred format. Download the desired PBD files and open them in a raster-based image editor. Now, isolate the colored pixels of interest from the top layer of the PBD file by adding a completely black layer below the colored in layer and a black mask layer above it, to exclude pixels outside the template body outline, creating a modified PBD with only the colored pixels within the body outline on a black background.

To export the processed PBDs as portable network graphic files, click on File, select Export, and choose Export As.Select PNG format, then click Export. Convert each pixel value within the PBD from RGB to HSV color space using the OpenCV2 Python package. Extract the hue value for every pixel by executing the Python scripts titled measure_sanoblur and rgba2hsv.

Run the quantifypain python script to calculate the three PBD metrics. First, calculate the PBD coverage by dividing the number of colored pixels by the total number of pixels within the body diagram. The range for females is 1 to 820, 452 pixels, and for males it is 1 to 724, 608 pixels.

Multiply the results by 100 to normalize data from a zero to 100 scale. Next, determine the PBD sum intensity by adding the hue values for all pixels in the body diagram, which is done by running rgba2hsv. The female range is 0 to 114, 45, 354, and the male range is 0 to 101, 82, 816.

Divide the result by the maximum PBD sum intensity and multiply by 100. The maximum PBD sum intensity depends on the total number of pixels in the body diagram multiplied by 139.5. Compute the PBD mean intensity by dividing the sum of all hue values by the total number of colored pixels.

Finally, process each PBD file and compile the outputs in a spreadsheet for further analysis. The PBD metrics were correlated with the Numerical Rating Intensity or NRS, Visual Analog Scale or VAS and McGill Pain Questionnaire or MPQ for most patients. In four of five patients, PBD metrics significantly shared mutual information or MI with the NRS VAS intensity, VAS unpleasantness and MPQ.

In all patients, the NRS shared significant MI with VAS intensity, VAS unpleasantness and MPQ, while the PBD sum shared MI with PBD coverage and PBD mean.