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Accepted for/Published in: JMIR Formative Research

Date Submitted: Jan 20, 2022
Date Accepted: May 16, 2022

The final, peer-reviewed published version of this preprint can be found here:

Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps

Dixit A, Lee M

Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps

JMIR Form Res 2022;6(6):e36687

DOI: 10.2196/36687

PMID: 35749160

PMCID: 9232214

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Development of an algorithm for the quantification of digital body maps for pain. 

  • Abhishek Dixit; 
  • Michael Lee

ABSTRACT

Background:

Pain is an unpleasant sensation that signals potential or actual bodily injury. The locations of bodily pain can be communicated and recorded by drawing onto a template, which are 2D or 3D (mannikin) surface maps. 'Free-hand' drawings are often part of validated pain questionnaires for example, the Brief Pain Inventory. They are increasingly acquired as digital images. However, there are no open source tools with which to summarize data on the topography of bodily pain in the form of spatial maps.

Objective:

We sought to provide tools that can generate a spatial (body) map showing where pain is commonly located. The tools must be able to process any number of individual drawings of pain made on any 2D body template.

Methods:

We designed a body pain map tool by using a body template, which is an adaptation of one of the oldest works of Sir Henry Head. The tool captured the participants' clicks on the body template in a MongoDB database. Each click created an SVG element with attributes x, y, width, height and region. Each participant’s pain drawing comprised of multiple overlapping rectangles. An algorithm was developed and implemented in the Python Programming Language (release 3.9) to compute the overlap of rectangles. The performance of the algorithm was assessed in terms of the Python script’s execution time. We used simulated datasets of overlapping rectangles from multiple drawings, and also pain drawings obtained from 23 participants who used the body pain map tool. The 23 participants were screened for a clinical drug trial (ISRCTN68734605).

Results:

The algorithm produces a heat map that comprises mutually exclusive rectangles. Each rectangle carries an overlap count or proportion which denotes locations common to specific participants, and therefore the output is compressed because each spatial location (a unique rectangle) is not a pixel, but a collection of pixels. When transformed into an SVG (or an SVG + HTML) file, the output is feasibly rendered as a heat map on standard web browsers. The layout (vertical/horizontal) of the rectangles used for the heat map can be specified depending upon the dimensions of the body regions. The output from the Python script can also be filtered by providing a range for the overlap count and thresholds for width and height of rectangles. The output can also be exported to a CSV file as required for further analysis.

Conclusions:

Whilst further validation in much larger actual (real) data sets is required, the Python scripts in their current form allow the generation of heat maps from any number of individual drawings on any standard 2D template of the body.


 Citation

Please cite as:

Dixit A, Lee M

Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps

JMIR Form Res 2022;6(6):e36687

DOI: 10.2196/36687

PMID: 35749160

PMCID: 9232214

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