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利用智能设备提高伤口面积测量精度和准确性的方法:高级应用程序 Planimator。

Ways to increase precision and accuracy of wound area measurement using smart devices: Advanced app Planimator.

机构信息

Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.

出版信息

PLoS One. 2018 Mar 5;13(3):e0192485. doi: 10.1371/journal.pone.0192485. eCollection 2018.

DOI:10.1371/journal.pone.0192485
PMID:29505569
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5837081/
Abstract

INTRODUCTION

Wound surface area measurement is important as therapeutic decisions may depend on the change of wound surface area over time. Digital planimetry is a popular technique in wound area measurement. It is accurate and repeatable when calibration is performed with 2 rulers placed at opposite sides of a wound. The aim of the current study was improving accuracy and precision of wound area measurement using capabilities of smart devices.

METHODS

The correction of area measurement based on calculated camera tilt angle and the calculation of calibration coefficient of linear dimensions as weighted average were proposed. These and other improvements were applied in the Planimator app for Android, which was then used in the study. Accuracy and precision of the Planimator app were compared to the Visitrak device, the SilhouetteMobile device, the AreaMe software, and to the digital planimetry based on 2-ruler calibration with pictures taken by the smartphone, compact, and D-SLR cameras. Areas of 40 wound shapes of area ranged from 0.14 to 31.72 cm2 were measured with each device. Medians of relative errors (REs) were compared in the accuracy tests and standard deviations (SDs) of relative differences (RDs) were compared in the tests of precision.

RESULTS

The median of REs for the Planimator app was not significantly different from the medians of REs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the medians of REs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The SD of RDs for the Planimator app was not significantly different from the SDs of RDs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the SDs of RDs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The Planimator app installed at a smartphone revealed to be 2-fold more accurate and 1.5-fold more precise than the measurements with using ImageJ software based on pictures taken with the same smartphone.

CONCLUSIONS

The Planimator app occurred to have the same accuracy and precision as measurements with digital planimetry with 2-ruler calibration and based on pictures from a compact camera or a D-SLR camera. This app showed better accuracy and precision than the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera.

摘要

简介

创面面积测量很重要,因为治疗决策可能取决于创面面积随时间的变化。数字描记术是一种常用的创面面积测量技术。当使用放置在创面相对两侧的 2 个标尺进行校准时,它是准确且可重复的。本研究的目的是利用智能设备的功能提高创面面积测量的准确性和精密度。

方法

提出了基于计算的相机倾斜角的面积测量校正和线性尺寸校准系数的计算作为加权平均值。这些改进以及其他改进应用于适用于 Android 的 Planimator 应用程序中,然后在该研究中使用。Planimator 应用程序的准确性和精密度与 Visitrak 设备、SilhouetteMobile 设备、AreaMe 软件以及基于智能手机拍摄的照片的 2 标尺校准的数字描记术、紧凑型相机和数码单反相机进行了比较。使用每种设备测量了 40 个面积范围在 0.14 至 31.72 cm2 的创面形状的面积。在准确性测试中比较了相对误差(RE)的中位数,在精密度测试中比较了相对差异(RD)的标准偏差(SD)。

结果

Planimator 应用程序的 RE 中位数与基于来自紧凑型或数码单反相机的照片的数字描记术的 RE 中位数没有显著差异,但明显低于 Visitrak 和 SilhouetteMobile 设备、AreaMe 软件和基于智能手机拍摄的照片的数字描记术的 RE 中位数。Planimator 应用程序的 RD 的 SD 与基于来自紧凑型或数码单反相机的照片的数字描记术的 RD 的 SD 没有显著差异,但明显低于 Visitrak 和 SilhouetteMobile 设备、AreaMe 软件和基于智能手机拍摄的照片的数字描记术的 RD 的 SD。安装在智能手机上的 Planimator 应用程序被发现比使用相同智能手机拍摄的图像的基于 ImageJ 软件的测量结果更准确,其准确性提高了 2 倍,精密度提高了 1.5 倍。

结论

Planimator 应用程序的准确性和精密度与基于 2 标尺校准的数字描记术和基于来自紧凑型相机或数码单反相机的照片的数字描记术相同。该应用程序的准确性和精密度优于 Visitrak 和 SilhouetteMobile 设备、AreaMe 软件以及基于智能手机拍摄的照片的数字描记术。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/e2750f615b25/pone.0192485.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/20622e80c090/pone.0192485.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/d6297b462ab9/pone.0192485.g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/3e239a5cabde/pone.0192485.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/dd16d599369e/pone.0192485.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/e2750f615b25/pone.0192485.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/20622e80c090/pone.0192485.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/46c901289efc/pone.0192485.g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b830/5837081/e2750f615b25/pone.0192485.g008.jpg

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