Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps.
Quantification of Digital Body Maps for Pain: Development and Application of an Algorithm for Generating Pain Frequency Maps.
复制标题
DOI：
10.2196/36687
复制
发表时间：
2022-06-24
影响因子：
2.2
通讯作者：
Lee, Michael
中科院分区：
文献类型：
--
作者：
Dixit, Abhishek;Lee, Michael
关键词：
疼痛是一种不愉快的感觉，预示着潜在的或实际的身体伤害。身体疼痛的位置可以通过手绘在2D或3D（人体模型）表面地图上进行交流和记录。徒手疼痛图通常是有效疼痛问卷（例如，简短疼痛清单）的一部分，并使用未划分身体轮廓的2D模板。对图纸的同时分析可以生成疼痛频率图，这在临床上对于识别疾病中常见疼痛的区域是有用的。基于网格的方法（将模板划分为单元格）允许轻松生成疼痛频率图，但是网格的粒度会影响数据捕获的准确性和最终用户的可用性。无网格模板规避了与网格创建和选择相关的问题，并为最类似于纸质图纸的图纸提供了公正的基础。然而，绘制区域的精确捕获在制作疼痛频率图方面提出了相当大的挑战。虽然基于web的应用程序和基于移动的应用程序可以广泛地用于手绘数字绘图，但缺乏从无网格绘图生成疼痛频率图的工具。我们试图提供一种算法，可以处理任意数量的无网格2D身体模板上的手绘图，以生成疼痛频率图。我们设想在临床或研究环境中使用该算法，以促进疾病诊断或疾病之间人类疼痛解剖学的细粒度比较，或作为指导监测或发现治疗的结果指标。我们设计了一个基于网络的工具，使用无网格2D身体模板捕获徒手疼痛图。每个绘图由重叠的矩形（可缩放矢量图形<rect>元素）组成，通过在主体模板的同一区域涂鸦创建。在Python中开发并实现了一种算法来计算矩形的重叠并生成疼痛频率图。从2个临床数据集获得的图纸上证明了该算法的实用性，其中一个是临床药物试验（ISRCTN68734605）。我们还使用重叠矩形的模拟数据集来评估算法的性能。该算法生成了代表身体模板上唯一位置的非重叠矩形。每个矩形都有一个重叠频率，表示在该位置有疼痛的参与者的数量。当转换成HTML文件时，输出可以在web浏览器上呈现为疼痛频率图。输出矩形的布局（垂直-水平）可以根据主体区域的尺寸来指定。输出还可以导出为CSV文件，以便进一步分析。虽然需要在更大的临床数据集中进一步验证，但目前形式的算法允许从任何2D身体模板上的任意数量的手绘图生成疼痛频率图。
Pain is an unpleasant sensation that signals potential or actual bodily injury. The locations of bodily pain can be communicated and recorded by freehand drawing on 2D or 3D (manikin) surface maps. Freehand pain drawings are often part of validated pain questionnaires (eg, the Brief Pain Inventory) and use 2D templates with undemarcated body outlines. The simultaneous analysis of drawings allows the generation of pain frequency maps that are clinically useful for identifying areas of common pain in a disease. The grid-based approach (dividing a template into cells) allows easy generation of pain frequency maps, but the grid’s granularity influences data capture accuracy and end-user usability. The grid-free templates circumvent the problem related to grid creation and selection and provide an unbiased basis for drawings that most resemble paper drawings. However, the precise capture of drawn areas poses considerable challenges in producing pain frequency maps. While web-based applications and mobile-based apps for freehand digital drawings are widely available, tools for generating pain frequency maps from grid-free drawings are lacking. We sought to provide an algorithm that can process any number of freehand drawings on any grid-free 2D body template to generate a pain frequency map. We envisage the use of the algorithm in clinical or research settings to facilitate fine-grain comparisons of human pain anatomy between disease diagnosis or disorders or as an outcome metric to guide monitoring or discovery of treatments. We designed a web-based tool to capture freehand pain drawings using a grid-free 2D body template. Each drawing consisted of overlapping rectangles (Scalable Vector Graphics <rect> elements) created by scribbling in the same area of the body template. An algorithm was developed and implemented in Python to compute the overlap of rectangles and generate a pain frequency map. The utility of the algorithm was demonstrated on drawings obtained from 2 clinical data sets, one of which was a clinical drug trial (ISRCTN68734605). We also used simulated data sets of overlapping rectangles to evaluate the performance of the algorithm. The algorithm produced nonoverlapping rectangles representing unique locations on the body template. Each rectangle carries an overlap frequency that denotes the number of participants with pain at that location. When transformed into an HTML file, the output is feasibly rendered as a pain frequency map on web browsers. The layout (vertical-horizontal) of the output rectangles can be specified based on the dimensions of the body regions. The output can also be exported to a CSV file for further analysis. Although further validation in much larger clinical data sets is required, the algorithm in its current form allows for the generation of pain frequency maps from any number of freehand drawings on any 2D body template.
登录
查看更多内容
影响因子：
4.8
作者：
Bernard Healey SA;Scholtes I;Abrahams M;McNaughton PA;Menon DK;Lee MC
通讯作者：
Lee MC
影响因子：
3.6
作者：
Barbero, M.;Moresi, F.;Falla, D.
通讯作者：
Falla, D.
DOI：
10.1002/ejp.1688
发表时间：
2021-03
期刊：
European journal of pain (London, England)
影响因子：
--
作者：
Ali SM;Lau WJ;McBeth J;Dixon WG;van der Veer SN
通讯作者：
van der Veer SN
影响因子：
3.7
作者：
Alter BJ;Anderson NP;Gillman AG;Yin Q;Jeong JH;Wasan AD
通讯作者：
Wasan AD
DOI：
10.1111/papr.12581
发表时间：
2018-01
期刊：
Pain practice : the official journal of World Institute of Pain
影响因子：
--
作者：
Cruder C;Falla D;Mangili F;Azzimonti L;Araújo LS;Williamon A;Barbero M
通讯作者：
Barbero M