Three-dimensional imaging is a novel and reliable technique to measure total body surface area

Evolution of Burn Care: Past, Present, and Future

Burn care evolved slowly from primitive treatments depicted in cave drawings 3500 years ago to a vibrant medical specialty which has made remarkable progress over the past 200 years. This evolution involved all areas of burn care including superficial dressings, wound assessment, fluid resuscitation, infection control, pathophysiology, nutritional support, burn surgery, and inhalation injury. Major advances that contributed to current standards of care and improved outcomes are highlighted in this article. New innovations are making possible a future where severe burn injuries will require less morbid interventions for acute care and outcomes will restore patients more closely to their pre-injury condition.

A clinimetric assessment of the validity and reliability of 3D technology for scar surface area measurement

INTRODUCTION

The quality of scars has become an important outcome of burn care. Objective scar assessment through scar surface area measurement enables quantification of scar formation and evaluation of treatment efficacy. 3D technology has proven valid and reliable but often remains cumbersome, expensive, and time-consuming. 3D technology with depth sensors on mobile devices has become available and might surpass these limitations. This study provides a clinimetric assessment of the validity and reliability of a 3D system with a depth sensor for scar surface area measurement.

METHODS

A technology involving a depth sensor mounted on a mobile device was used. Images and analyses were made with a custom-made software application. A standardized one-keyframe image capturing procedure was followed. To assess validity, stickers with predefined dimensions (8.01 cm² - 77.70 cm²) were imaged in a single observer setting on various body parts of healthy volunteers. To assess reliability, hypertrophic scars, keloids, and normotrophic scars were imaged and rated by two observers independently. Data are expressed as mean (+/-SD), Coefficient of Variation (CV), Intraclass Correlation Coefficients (ICC), and Limits of Agreements (LoA).

RESULTS

Eighty stickers placed on 20 healthy volunteers showed validity with CV between 0.62%- 1.67% for observer A and 0.75%- 1.19% for observer B. For the reliability study, 69 scars on 36 patients were included. Mean scar surface area ranged from 0.83 cm² to 155.59 cm². Mean scar surface area measurement was 13.83 cm² (SD 23.06) for observer A and 13.59 cm² (SD 23.31) for observer B. Adjusted interobserver CV for trained observers is estimated as 5.59%, with corresponding LoA = 0 ± 0.15 x mean surface area. Interobserver ICCs were 0.99-1.00.

CONCLUSION

This 3D technology with a depth sensor for measuring scar surface area provides valid and reliable data and thereby surpasses expensive and time-consuming 3D cameras.

An Integrated Hardware and Software Application to Support Wound Measurement Using a 3D Scanner and Image Processing Techniques

Aim:

To build wounds volume(3D) and area(2D) measuring system and device.

Background:

The measurement of the wound depth has been troublesome due to difficulty fo the procedures, physicians mostly avoid inspecting the wound depth as it could cause wound inflammation and infection.

Objective:

To build a contactless device for measuring wound volume and develop the system to support the wound treatment process which offers precise measurement and wound healing progression.

Methods:

Build a machine to control and stabilize 3D-scanner over the wound using a servo motor and apply the image processing technique to calculate the wound's area and volume. Comparing the machine accuracy by using Archimedes's principle testing with various wound model sizes, made from folding clay and pork rinds.

Results:

The device and system generate an error value of less than 15% which is within a satisfactory level.

Conclusion:

Knowing the wound depth is vital for the treatment, direct contact to the wound area can cause inflammation, infection, and increase time to heal. This device will help physicians to get more insight into the wound and improve the treatment plan for the patients.

There are certain limitations to be considered for future work. Firstly, different software components used in the image processing and estimation process could be integrated to enhance user experience. Secondly, it is possible to apply Machine Learning techniques to identify the wounded area on the wound image file.