Abstract
Chronic wounds are common in clinical practice, with long treatment cycle and high treatment cost. Changes in wound area can well predict the effectiveness of treatment and the possibility of healing. Therefore, continuous wound monitoring and evaluation are particularly important. Traditional manual wound measurement tends to overestimate wound area. Recently, various intelligent wound measurement devices have been introduced into clinical practice. This review aims to summarise the reliability, validity, types and measurement principles of different intelligent wound measurement devices, so as to analyse the clinical value and application prospect. Articles numbering 2610 were retrieved from the database, and 14 articles met the inclusion criteria. The results showed that the intelligent wound measurement devices included in the study reported good reliability and validity. Contact devices can lead to wound bed damage, wound deformation, patient pain, and is not convenient for electronic wound recording; partial contact devices can complete continuous monitoring and recording of wounds, but are not sensitive to wound depth measurement. Non‐contact devices are more accurate in capturing wound images. In addition to wound measurement, they also have the function of wound assessment. In general, handheld and portable non‐contact devices have great clinical value and promotion prospects.
Keywords: chronic wound, intelligent device, scoping review, wound measurement
1. INTRODUCTION
Diabetic foot ulcer (DFU), venous leg ulcer (VLU) and pressure ulcer (PU) are the most common chronic wounds in clinic. Epidemiological data show that the global prevalence of DFUs is as high as 6.3%. 1 VLUs affect up to 49 million people worldwide each year, with a cumulative lifetime risk of 1.0%–1.8%. 2 The global prevalence of PUs is 12.8%, and the incidence of hospital‐acquired PUs is 8.4%. 3 Furthermore, it is estimated that the incidence and prevalence of chronic wounds will continue to rise, consistent with the overall increase of chronic degenerative diseases and life expectancy. 4
Due to factors such as wound pain, limited mobility, and psychological disorders, the health‐related quality of life of patients with chronic wounds is quite low. 5 Additionally, few studies have counted the indirect csosts associated with wounds (such as absenteeism, nutrition, and transportation), but the direct cost of chronic wounds is quite expensive. Taking VLUs as an example, in the UK, the direct cost per VLU patient is £ 7706 per year. 6 In Australia, the direct cost per VLU patient ranges from $ 214.61 to $ 294.72 per week. 7 In the USA, the annual medical and commercial insurance costs for VLU patients rise to $ 18 986 and $ 13 653 respectively, which means that the annual medical burden is as high as $ 14.9 billion. 8
A guideline published by the University of Pittsburgh points out that changes in wound area within 4 weeks of treatment can well predict the effectiveness of treatment and the possibility of healing. 9 A study confirmed that if ineffective treatment is stopped for ulcers with a low likelihood of healing, the average cost per patient can be saved by about $ 12 600. 10 Moreover, wound assessment is also helpful for early identification and timely treatment of complications. Manual measurement with a wound ruler is the most commonly used wound measurement method in clinical practice, and the wound area is calculated according to the product of the longest length and the widest width (L × W). However, manual measurement overestimates the wound area by an average of 41%. 11 Considering the long treatment cycle of chronic wounds, it is extremely important to accurately measure and record wound changes.
The accuracy of wound measurement directly affects clinical diagnosis and treatment. Therefore, wound measurement devices should be scientifically selected to improve the accuracy of measurement. With the development of photography technology and artificial intelligence, intelligent wound measurement devices have experienced a breakthrough from 2D to 3D. However, intelligent wound measurement devices have not been widely used. Whether these intelligent wound measurement devices are superior to traditional manual measurement, and whether there is a need for further promotion, is not yet fully clear. This study reviewed the scope of different intelligent wound measurement devices from the perspective of reliability, validity, types and principles, and analysed their clinical value and application prospects, in order to provide reference for government health policy makers and clinical staff.
2. METHODS
2.1. Study design
Based on the methodological framework proposed by Arksey and O'Malley, 12 this scoping review was performed. In summary, this methodological framework involves six corn stages: (a) identifying the research question; (b) identifying relevant studies; (c) study selection; (d) charting the data; (e) collating, summarising and reporting the results; (f) consultations with consumers, stakeholders and policy makers to retrieve relevant references and insights beyond the literature. In order to reduce bias, the Preferred Reporting Items for Systematic Reviews and Meta‐analyses for Scoping Reviews 13 was adopted to optimise this study. Before the literature search, the research team identified specific research questions: What are the reliability, validity, types and principles of different intelligent wound measurement devices?
2.2. Literature search
Literature search was conducted in the following databases: MEDLINE, BMJ, EMBASE, CINAHL and PubMed. We also retrieved grey literature as a supplement by hand. Literature retrieval was conducted by the following keyword alone or in combination: wound, ulcer, dimension, size, area, volume, measurement. As recommended by JBI, inclusion and exclusion criteria were formulated by the PCC mnemonic (P for participants, C for concept, and C for context). 14 The participants included all patients with chronic wounds. The concept refers to intelligent wound measuring devices, such as contact, partial contact and non‐contact devices. As for context, including any healthcare setting. The initial search was completed on January 20, 2023, and updated on January 1, 2024 to ensure that no newly published literature was missed.
2.3. Inclusion and exclusion criteria
The aim of this study is to integrate intelligent wound measurement devices that is close to clinical practice. Based on the study aim, strict inclusion and exclusion criteria were formulated. Literatures were included if they met the following inclusion criteria: (a) focused on the reliability and validity of intelligent wound measurement devices, (b) performed wound measurement for chronic wounds, (c) conducted original research, (d) full texts available in English. Exclusion criteria were as follows: (a) the type of study involved case reports, conference papers, non‐clinical research reports, and reviews; (b) the type of wounds involved animal wounds, wound moulds, and skin diseases; (c) the type of devices was not mentioned.
2.4. Study selection
All retrieved literatures were imported into NoteExpress, through which duplicates were deleted. Then, manual screening was performed based on inclusion and exclusion criteria, which mainly consisted of two steps. First, read the title and abstract to preliminarily exclude literatures, and then read the full text to eliminate the literatures that do not meet the requirements. The literature screening process was mainly completed by the two authors (YW and LW) independently. If there were any differences, the research team discussed and determined it. In order to improve the accuracy of literature selection, all members of the research team had studied evidence‐based nursing courses and were familiar with the literature screening process.
2.5. Data extraction
The research team discussed and developed three data extraction tables. The first table provides general information on included studies (country, year, study type, sample size and wound type). The second table mainly extracts the reliability and validity of different intelligent wound measurement devices. According to the 95% confidence interval of ICC estimates, values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 represent poor, medium, good and excellent reliability, respectively. 15 Regarding the construct validity, KMO values less than 0.6, between 0.6 and 0.7, between 0.7 and 0.8, and greater than 0.8, respectively, indicate that it is not suitable, more suitable, suitable, and very suitable for extracting information. If the corresponding relationship between the item and the factor is basically consistent with the research psychological expectation, the validity is good. 16 The third table is dedicated to demonstrating types and principles of different intelligent wound measurement devices. All data were extracted by one researcher (YW) and checked by another researcher (LW). If there was a conflict of opinion, a third researcher (MY) would participate in the consultation and made a final decision.
3. RESULTS
A total of 2610 literatures were retrieved from the preliminary search. After step‐by‐step screening, 14 literatures 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 met the inclusion criteria. The literature screening process is shown in Figure 1.
FIGURE 1.
Flow chart of literature screening.
3.1. Characteristics of the included studies
A total of 14 studies published between 2007 and 2023 were finally included, and most of them (n = 9) were published in the past 5 years. The studies originated from eight different continents: Singapore (n = 3), Australia (n = 3), USA (n = 2), Brazil (n = 2), France (n = 1), Turkey (n = 1), Denmark (n = 1), Japan (n = 1). Among the included studies, 13 were analytical studies (11 cross‐sectional studies and two cohort studies), and one was observational study (one descriptive study). The sample size of the included studies fluctuated between 13 and 115, and the wound type covered DFUs, VLUs and PUs. The general characteristics of included studies are shown in Table 1.
TABLE 1.
General characteristics of the included studies.
| Author | Country | Year | Study type | Sample size | Wound type |
|---|---|---|---|---|---|
| Fong et al. 19 | Singapore | 2023 | Cohort study | 82 | VLUs |
| Chan et al. 20 | Singapore | 2022 | Cross‐sectional study | 28 | DFUs |
| Chan et al. 21 | Singapore | 2022 | Cross‐sectional study | 52 | VLUs |
| Biagioni et al. 22 | Brazil | 2021 | Cross‐sectional study | 85 | Wounds caused by vascular diseases |
| Kuang et al. 23 | Australia | 2021 | Cross‐sectional study | 115 | DFUs |
| Lasschuit et al. 24 | Australia | 2021 | Cross‐sectional study | 63 | DFUs |
| Do Khac et al. 25 | France | 2021 | Cross‐sectional study | 61 | PUs |
| Pena et al. 18 | Australia | 2020 | Cross‐sectional study | 57 | DFUs |
| Toygar et al. 26 | Turkey | 2020 | Cross‐sectional study | 20 | DFUs |
| Jørgensen et al. 27 | Denmark | 2018 | Cross‐sectional study | 48 | The majority were DFUs (42/48) |
| Eberhardt et al. 28 | Brazil | 2016 | Cross‐sectional study | 36 | VLUs |
| Davis et al. 29 | USA | 2013 | Cohort study | 13 | PUs |
| Gardner et al. 30 | USA | 2012 | Cross‐sectional study | 33 | DFUs |
| Sugama et al. 17 | Japan | 2007 | Correlational study | 40 | PUs |
Abbreviations: DFUs, diabetic foot ulcers; PUs, pressure ulcers; VLUs, venous leg ulcers.
3.2. Reliability and validity of different intelligent wound measurement devices
All included studies reported reliability, only two studies reported validity, and the reported reliability and validity values were far greater than 0.75. The measurement dimension of intelligent wound measurement devices involves length, width, area and volume. In some studies, wounds were divided according to wound area (<10 or >10 cm2) and shape (regularly shaped or irregularly shaped), and then indepth reliability and validity evaluation were performed. The reliability and validity of different intelligent wound measurement devices are shown in Table 2.
TABLE 2.
Reliability and validity of different measurement devices.
| Author | Device | Reference | Inter‐rater reliability | Intra‐rater reliability | Validity |
|---|---|---|---|---|---|
| Fong et al. 19 | TA | Visual approximation/ruler |
Length (ICC = 0.843–0.947) Width (ICC = 0.808–0.890) Area (ICC = 0.701–0.864) |
Length (ICC = 0.981–0.987) Width (ICC = 0.973–0.981) Area (ICC = 0.977–0.984) |
– |
| Chan et al. 20 | C4W | Manual measurement |
Length (ICC = 0.825–0.934) Width (ICC = 0.825–0.930) Area (ICC = 0.872–0.932) |
Length (ICC = 0.956–0.993) Width (ICC = 0.933–0.963) Area (ICC = 0.984–0.994) |
– |
| Chan et al. 21 | WA | Manual measurement |
Length (ICC = 0.875–0.889) Width (ICC = 0.891–0.900) Area (ICC = 0.932–0.950) |
Length (ICC = 0.978–0.989) Width (ICC = 0.978–0.980) Area (ICC = 0.990–0.992) |
– |
| Biagioni et al. 22 | Imito | ImageJ | Area (ICC = 0.978) | – | – |
| Kuang et al. 23 | NDKare | Visitrak/WoundVue | Area (ICC = 0.980) | Area (ICC = 0.991) | – |
| Lasschuit et al. 24 | 3DWM | Manual measurement/ImageJ |
Depth (ICC = 0.87) Area (ICC = 0.97) Volume (ICC = 0.82) |
Depth (ICC = 0.88) Area (ICC = 0.96) Volume (ICC = 0.90) |
– |
| Do Khac et al. 25 | imtioMeasure | Ruler/acetate tracing |
Area (ICC = 0.98) Length (ICC = 0.99) Width (ICC = 0.97) |
Area (ICC = 0.99) Length (ICC = 0.99) Width (ICC = 0.98) |
Area (ICC = 0.97) Length (ICC = 0.97) Width (ICC = 0.95) |
| Pena et al. 18 | 3DWM | Visitrak |
Area (ICC = 0.983) Volume (ICC = 0.978) Depth (ICC = 0.975) |
Depth (ICC = 0.984) Area (ICC = 0.995) Volume (ICC = 0.988) |
– |
| Toygar et al. 26 | ImageJ/3DWM | Manual measurement | – |
3DWM/manual measurement (ICC = 0.970) Manual measurement/ImageJ (ICC = 0.995) |
– |
| Jørgensen et al. 27 | 3DWM | Digital imaging/gel injection |
Area (ICC = 0.996) Volume (ICC = 0.973) |
Area (ICC = 0.997) Volume (ICC = 0.975) |
– |
| Eberhardt et al. 28 | AutoCAD | Image Tool | – |
Area ≤10 cm2 (ICC = 0.960) Area >10 cm2 (ICC = 0.960) |
– |
| Davis et al. 29 | LifeViz 3D | – | Volume (ICC = 0.987) | – | – |
| Gardner et al. 30 | VeVMD | – | Volume (ICC = 0.970) | Volume (ICC = 0.981) | – |
| Sugama et al. 17 | VISITRAK | Digital planimetry | Area (ICC = 0.990) | Area (ICC = 0.990) | r = 0.99 |
Abbreviations: 3DWM, three‐dimensional wound measurement; C4W, CARES4WOUNDS; DEM, digital elliptical measurement; EM, elliptical measurement; SfM‐3DULC, structure from motion; TA, tissue analytics; WA, WoundAide.
3.3. Types and principles of different intelligent wound measurement devices
Intelligent wound measurement devices are divided into three types: contact, partial contact and non‐contact. The contact devices are measured by a three‐layer tracking grid sheet in contact with the wound, and rely on manual tracking. The representative device is VISITRAK. Partial contact devices calculate the wound area based on the calibration marker placed next to the wound. They rely on mobile application or software programme to measure wounds. There are many kinds of partial contact devices, such as tissue analytics (TA), imito, NDKare, VeVMD, ImageJ and AutoCAD. Non‐contact devices measure the wound through a three‐dimensional wound model. The projector, with the use of double or three cameras, can form a three‐dimensional wound model. There are also many non‐contact devices, such as CARES4WOUNDS, WoundVue, 3D‐WAM camera, WA and LifeViz 3D. The types and principles of different wound measurement devices are shown in Table 3.
TABLE 3.
Types and principles of different intelligent wound measurement devices.
| Contact | Types | Device | Principle |
|---|---|---|---|
| Contact | Manual tracing | VISITRAK | The VISITRAK system consists of three components: a tracking grid sheet composed of three layers (an outer layer, a wound contact layer, and a layer to be placed on the measurement device), a digital pad for calculating wound size based on tracking, and a sterile, disposable wound depth indicator. The outer layer of the tracing sheet is removed and the sterile wound contact layer is placed on the wound while its shape is traced. The wound contact layer is then discarded and the remaining layer containing the tracing is placed on the digital measuring pad. The outline of the wound is retraced into the measurement system using the accessory pen supplied with the system. The software (CAD, Smith & Nephew, London, UK) in the measurement pad responds to the signal from the coil housed in the accessory pen and calculates the area |
| Partial contact | Mobile application | TA | TA can be run as a smartphone app on iOS and Android devices. Calibration marker is placed next to the patient's wound, and a wound video of about 5 s is taken. Machine learning and computer vision are used to generate 3D rendering of the wound, which is used to automatically define the wound boundary, and then report the measurement results |
| Mobile application | Imito | The camera of the smartphone is positioned 20–30 cm away from the wound and parallel to the wound, and the calibration marker is positioned in the same plane as the wound. The imito application recognises the calibration marker and takes a photo, then the operator can manually select the wound boundary, and then the imito application can report the two‐dimensional measurement results | |
| Mobile application | NDKare | A marker is placed near the wound as a ruler. A rectangular frame appears during the measurement, and the wound and mark should be included in the rectangular frame for calibration. The application automatically distinguishes the pixels of the wound area from the normal tissue. If needed, the user can outline the wound boundary on the mobile phone, thereby generating 2D measurements. For continuous wound imaging, a “ghost image” of the previous image of the wound will appear, and a timeline of each patient's wound, including photos and measurements, will be generated to monitor wound progression | |
| Software program | VeVMD | Canon camera (Canon USA, Lake Success, NY) is used to obtain 6 images including ulcer, 3 cm2 positioning card, subject ID number, and single‐point wound depth indicator. The image is uploaded to the VeVMD software program. A 3 cm2 positioning card is used to orient each image to the plane of the ruler and the ulcer. The image with the highest direction accuracy is selected for all subsequent wound measurements. The “Shrink‐Fit Tool” is used to track the contour of the ulcer, which outlines the edge of the ulcer by connecting the tracer points with line segments. uses “Line Tool” to measure the depth, which fits a straight line along the wound depth indicator. VeVMD uses the mathematical formula of the ellipsoid to estimate the volume | |
| Software programme | ImageJ | The ImageJ is a free image processing software developed by the National Institutes of Health (NIH). A sterile paper ruler is placed next to the wound and photographed with a standard camera. The known length is set in the “Set Scale” function of ImageJ, and the wound measurement parameters are automatically calculated by the known length | |
| Software programme | AutoCAD | A 3 cm black square is placed near the wound as a reference. A Fujifilm camera FinePix S14 megapixel (f/6.4, ISO 400, macro enabled, automatic white balance, high definition, flash disabled) is used to take photos perpendicular to the wound. Open the AutoCAD, click the edge of the wound through the “pick up point” function, create a closed multisegment line, and AutoCAD automatically calculates the wound area | |
| No contact | 3D structure sensor and dual camera | C4W | The C4W system consists of an iPad with a 3D structure sensor, an iPhone with a dual camera, and a Web‐based software application. The wound size is automatically measured by software algorithm, and the tissue types are classified according to epithelisation, granulation, necrosis and decay. The output data are recorded in real time according to the clinical workflow, and the patient data could be accessed remotely |
| Infrared projector and dual infrared camera | WoundVue | The WoundVue system consists of two infrared cameras and an infrared projector. The infrared camera images the wound from two different observation points, and the infrared projector projects a textured light pattern on the wound. The textured light pattern is beneficial to match the pixels in the first image with the corresponding pixels in the second image. Once the corresponding points are established, the triangulation process determines the range of all points in the image, thus realising the three‐dimensional reconstruction of the wound. After the user helps to outline the wound bed in the input image, the wound bed in the three‐dimensional model is closed with an artificial surface to facilitate the calculation of the area, volume and maximum depth of the wound | |
| Projector and three‐camera systems | 3D‐WAM camera | The 3D‐WAM camera consists of three cameras and a projector. A pattern composed of many points forms a grid and is projected onto the wound surface. Two of the cameras recognise the pattern and create a three‐dimensional geometry. A third camera creates a colour photo of the texture and adjusts it to a 3D geometry. The user sketches the edge through the pointing device on the computer display. The software calculates the measured value of the wound | |
| Structured light systems | WA | The WA imaging system consists of an iPad and a 3D depth‐sensing camera. The 3D camera uses a laser projector to project structured light. Thousands of invisible infrared points are projected onto the target object, and then the pattern changes and 3D data are recorded and captured by a frequency‐matched infrared camera. WA automatically detects the edge of the wound and measures the size of the wound through machine learning algorithms. The output data is automatically stored and can be used for remote evaluation or uploaded to the hospital electronic medical record system | |
| Stereophotogrammetric techniques | LifeViz 3D | The LifeViz 3D system consists of a digital camera equipped with a stereopair of lenses and a PC‐based computer application. The camera is equipped with a bifocal beam pointer to ensure that the distance and direction of the wound are consistent. The edge of the wound is identified in the software to generate a three‐dimensional reconstruction and calculate the surface area, length, width, average depth and volume of the wound |
Abbreviations: C4W, CARES4WOUNDS; TA, tissue analytics; WA, WoundAide.
4. DISCUSSION
Traditional measurement methods, such as wound ruler and probe, have the advantages of simple operation and low cost, and are still used in clinical practice. However, these traditional wound measurement methods are invasive and the measurement results are not accurate enough. 31 In order to optimise the wound measurement, intelligent wound measurement devices have been gradually introduced into clinical practice. In this study, a total of 12 intelligent wound measurement devices were summarised, which are divided into contact, partial contact and non‐contact devices.
Almost all the devices included in this study showed good results (ICC greater than 0.75), which initially confirmed its clinical application prospects. It is worth noting that the accuracy of the measurement results is affected by factors such as wound exudate and human body curvatures. 32 Moreover, the difference in wound measurement results is mainly due to the different subjective recognition of the wound edge. 33 In traditional wound care, since the wound ruler is not sterile, the wound measurement is usually carried out first, and then the wound care is carried out. Therefore, wound exudate may have a certain impact on the measurement results. On the contrary, the intelligent wound measurement devices are measured in a sterile or non‐contact wound manner, allowing the wound to be cleaned first and then measured, which can reduce the impact of exudation. Unfortunately, intelligent wound measurement devices are also limited by human body curvatures, which needs to be further optimised.
VISITRAK is a contact wound measurement system. VISITRAK's wound measurement process is divided into two steps. First, the wound edge is described by a tracking grid sheet, and then the wound area is automatically calculated by the digital pad. Compared to traditional manual wound measurement, the multilayer structure can reduce the risk of contamination. Compared to digital plane measurement, VISITRAK takes less time to measure wounds. 17 Unfortunately, VISITRAK also has some obvious drawbacks. First of all, because this measurement method is contact‐based, its main disadvantages are wound‐bed damage, wound deformation and patient pain. 34 Additionally, wound records have gradually shifted from paper documents to electronic records, which is conducive to wound dynamic monitoring and multidisciplinary teamwork. 35 However, VISITRAK cannot meet the requirements of uploading wound images. With the improvement of medical experience and the construction of hospital informatisation, the application and promotion of VISITRAK is limited.
Partial contact wound measurement devices rely on a mobile application (such as TA, imito, NDKare) or software program (such as VeVMD, ImageJ, AutoCAD) for wound measurement. Most of the partial contact wound measurement devices need to be paid for use. When measuring, a calibration indicator needs to be placed next to the wound. Because the calibration indicator is a two‐dimensional plane, it cannot measure the depth of the wound. Only VeVMD can achieve wound depth measurement through a depth indicator. Chronic wounds need long‐term treatment. At present, the application of wound follow‐up has been developed. 36 , 37 Its main functions include health education, notification reminder, wound image collection and telemedicine guidance, but it lacks wound measurement and recording functions. Participatory healthcare approaches have been shown to increase patient engagement by improving shared decision‐making processes. 38 Future studies can integrate the functions of wound follow‐up application and wound measurement application, encourage patients to participate in wound management, realise hospital‐family linkage, and reduce the cost of transportation and time for medical treatment.
Although partial contact intelligent wound measurement devices are easy to use in clinical practice, some wounds may not be completely captured in a single image. Non‐contact intelligent wound measurement devices can make up for this deficiency by measuring the wound based on the constructed three‐dimensional wound model. Non‐contact devices include CARES4WOUNDS, WatchVue, 3D‐WAM camera, WA and LifeViz 3D. In addition to measuring the wound area, non‐contact devices can also classify the wound tissue. Although shallow flat wounds are common in clinical practice, the three‐dimensional wound model has low accuracy in measuring such wounds. 18 Considering that patients with chronic wounds are inconvenient to move, and the existing non‐contact devices are mostly complex in structure, the development of handheld or portable devices have great clinical application prospects. 39 , 40
5. IMPLICATIONS FOR FUTURE RESEARCH
Intelligent wound measurement devices can minimise the subjectivity between users and allow continuous wound assessment and recording, which is beneficial in clinic. we also found some problems worthy of further exploration. There are significant differences in the measurement results between professionals and non‐professionals, 41 suggesting that intelligent wound measurement devices should be operated by trained clinical professionals. However, there is no consensus on device training for medical staff, and there is no standard clinical operation process. In addition, the sample size of the included studies is relatively small, and there is a lack of multicentre, large‐sample clinical research, especially the lack of patient experience research.
6. CONCLUSION
In this scoping review, we integrated 12 intelligent wound measurement devices and divided them into three categories based on the contact method. In addition to the reliability, validity and measurement principle, we also summarised the advantages, disadvantages and promotion prospects of different wound measurement devices, which can provide reference for the selection of intelligent wound measurement devices in clinical practice. It is suggested that future research should improve intelligent wound measurement devices from three perspectives: function optimisation, clinical operator training, and patient experience improvement, so as to make it more suitable for clinical needs.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ACKNOWLEDGEMENTS
There are no acknowledgements.
Wu Y, Wu L, Yu M. The clinical value of intelligent wound measurement devices in patients with chronic wounds: A scoping review. Int Wound J. 2024;21(3):e14843. doi: 10.1111/iwj.14843
DATA AVAILABILITY STATEMENT
There is no new data generation and analysis in this study, and data sharing is not applicable to this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
There is no new data generation and analysis in this study, and data sharing is not applicable to this study.