Development and validation of an algorithm to predict the treatment modality of burn wounds using thermographic scans: Prospective cohort study

The role of thermography in assessment of wounds. A scoping review

Assessment of wounds based on visual appearance has poor inter- and intra-rater reliability and it is difficult to differentiate between inflammation and infection. Thermography is a user-friendly quantitative image technique that collects the skin surface temperature pattern of the wound area and immediately visualizes the temperatures as a rainbow coloured diagram. The aim of this scoping review is to map and summarize the existing evidence on how thermography has been used to assess signs of inflammation in humans and animals with surgical or traumatic wounds. The method follows the Joanna Briggs Institute methodology. The databases searched were PubMed, Embase, CINAHL and Cochrane Library. 3798 sources were identified, 2666 were screened on title and abstract, 99 on full text and 19 studies were included for review. We found that the literature is diverse and originates from a variety of scientific fields. Thermography has been used to detect and predict inflammation and infection in surgical wounds. Grading systems based on the visual appearance correlate to temperature patterns detected with thermography. The general tendency is that thermography detects the temperature in a wound with inflammation to be warmer than a reference area or the same skin area before surgery. In a surgical wound the temperature is elevated 1-2 weeks after surgery due to natural physiological inflammation that induces healing, after 2 weeks the temperature of the wound area slowly and steady decreases to baseline over 1-3 months. If a secondary temperature peak happens during the healing phase of a surgical wound, it is likely that infection has occurred. Modern handheld thermographic cameras might be a promising tool for the clinician to quickly quantify the temperature pattern of surgical wounds to distinguish between inflammation and infection. However, firm evidence supporting infection thermography surveillance of surgical wounds as a technique is missing.

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

OBJECTIVE

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

Review of machine learning for optical imaging of burn wound severity assessment

Significance

Over the past decade, machine learning (ML) algorithms have rapidly become much more widespread for numerous biomedical applications, including the diagnosis and categorization of disease and injury.

Aim

Here, we seek to characterize the recent growth of ML techniques that use imaging data to classify burn wound severity and report on the accuracies of different approaches.

Approach

To this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds.

Results

The majority of these reports used digital color photographs as input data to the classification algorithms, but recently there has been an increasing prevalence of the use of ML approaches using input data from more advanced optical imaging modalities (e.g., multispectral and hyperspectral imaging, optical coherence tomography), in addition to multimodal techniques. The classification accuracy of the different methods is reported; it typically ranges from ∼ 70 % to 90% relative to the current gold standard of clinical judgment.

Conclusions

The field would benefit from systematic analysis of the effects of different input data modalities, training/testing sets, and ML classifiers on the reported accuracy. Despite this current limitation, ML-based algorithms show significant promise for assisting in objectively classifying burn wound severity.

Thermal Imaging as a Method to Indirectly Assess Peripheral Vascular Integrity and Tissue Viability in Veterinary Medicine: Animal Models and Clinical Applications

Infrared thermography (IRT) is a technique that indirectly assesses peripheral blood circulation and its resulting amount of radiated heat. Due to these properties, thermal imaging is currently applied in human medicine to noninvasively evaluate peripheral vascular disorders such as thrombosis, thromboembolisms, and other ischemic processes. Moreover, tissular damage (e.g., burn injuries) also causes microvasculature compromise. Therefore, thermography can be applied to determine the degree of damage according to the viability of tissues and blood vessels, and it can also be used as a technique to monitor skin transplant procedures such as grafting and free flaps. The present review aims to summarize and analyze the application of IRT in veterinary medicine as a method to indirectly assess peripheral vascular integrity and its relation to the amount of radiated heat and as a diagnostic technique for tissue viability, degree of damage, and wound care.

Predicting amputation using machine learning: A systematic review

Amputation is an irreversible, last-line treatment indicated for a multitude of medical problems. Delaying amputation in favor of limb-sparing treatment may lead to increased risk of morbidity and mortality. This systematic review aims to synthesize the literature on how ML is being applied to predict amputation as an outcome. OVID Embase, OVID Medline, ACM Digital Library, Scopus, Web of Science, and IEEE Xplore were searched from inception to March 5, 2023. 1376 studies were screened; 15 articles were included. In the diabetic population, models ranged from sub-optimal to excellent performance (AUC: 0.6-0.94). In trauma patients, models had strong to excellent performance (AUC: 0.88-0.95). In patients who received amputation secondary to other etiologies (e.g.: burns and peripheral vascular disease), models had similar performance (AUC: 0.81-1.0). Many studies were found to have a high PROBAST risk of bias, most often due to small sample sizes. In conclusion, multiple machine learning models have been successfully developed that have the potential to be superior to traditional modeling techniques and prospective clinical judgment in predicting amputation. Further research is needed to overcome the limitations of current studies and to bring applicability to a clinical setting.

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review

Significance: As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at providing an in-depth overview of the relevant methodologies of artificial intelligence (AI) and their potential implementation to support these growing needs of wound care and management. Recent Advances: MEDLINE, Compendex, Scopus, Web of Science, and IEEE databases were all searched for new AI methods or novel uses of existing AI methods for the diagnosis or management of hard-to-heal wounds. We only included English peer-reviewed original articles, conference proceedings, published patent applications, or granted patents (not older than 2010) where the performance of the utilized AI algorithms was reported. Based on these criteria, a total of 75 studies were eligible for inclusion. These varied by the type of the utilized AI methodology, the wound type, the medical record/database configuration, and the research goal. Critical Issues: AI methodologies appear to have a strong positive impact and prospects in the wound care and management arena. Another important development that emerged from the findings is AI-based remote consultation systems utilizing smartphones and tablets for data collection and connectivity. Future Directions: The implementation of machine-learning algorithms in the diagnosis and managements of hard-to-heal wounds is a promising approach for improving the wound care delivered to hospitalized patients, while allowing health care professionals to manage their working time more efficiently.

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery.

METHODS

A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application.

RESULTS

The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice.

CONCLUSIONS

The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.

A Randomized Controlled Trial on the Effect of Local Insulin Glargine on Venous Ulcer Healing

INTRODUCTION

To determine whether the local administration of insulin glargine compared with placebo in nondiabetic patients with venous ulcers (VUs) leads to increased wound healing.

METHODS

A randomized controlled trial using a split-plot design was performed in 36 adults with leg VUs >25 cm² and more than 3 mo of evolution. Each hemi-wound received either 10 UI insulin glargine or saline solution once a day for 7 d. Size of the wounds, thermal asymmetry, the number of blood vessels, and the percentage area of collagen content in wound biopsies were assessed at baseline and after 7 d of treatment. Blood capillary glucose was monitored once a day after the insulin injection.

RESULTS

After 7 d of treatment, the hemi-wounds treated with insulin glargine were significantly smaller, had less thermal asymmetry, more blood vessels, and more collagen content than the saline-treated side. Correlation between thermal asymmetry and the number of blood vessels was also found (r² = 66.2, P < 0.001). No patient presented capillary glucose levels ≤3.3 mmol/L nor any adverse effects.

CONCLUSIONS

In nondiabetic patients with chronic VUs, the topical administration of insulin glargine seems to be safe and promotes wound healing and tissue repair after 7 d of treatment.

Thermotropic Liquid Crystals for Temperature Mapping

Wound management in Space is an important factor to be considered in future Human Space Exploration. It demands the development of reliable wound monitoring systems that will facilitate the assessment and proper care of wounds in isolated environments, such as Space. One possible system could be developed using liquid crystal films, which have been a promising solution for real-time in-situ temperature monitoring in healthcare, but they are not yet implemented in clinical practice. To progress in the latter, the goal of this study is twofold. First, it provides a full characterization of a sensing element composed of thermotropic liquid crystals arrays embedded between two elastomer layers, and second, it discusses how such a system compares against non-local infrared measurements. The sensing element evaluated here has an operating temperature range of 34–38°C, and a quick response time of approximately 0.25 s. The temperature distribution of surfaces obtained using this system was compared to the one obtained using the infrared thermography, a technique commonly used to measure temperature distributions at the wound site. This comparison was done on a mimicked wound, and results indicate that the proposed sensing element can reproduce the temperature distributions, similar to the ones obtained using infrared imaging. Although there is a long way to go before implementing the liquid crystal sensing element into clinical practice, the results of this work demonstrate that such sensors can be suitable for future wound monitoring systems.

Infrared Thermography in Wound Care, Surgery, and Sports Medicine: A Review

For many years, the role of thermometry was limited to systemic (core body temperature) measurements (e.g., pulmonary catheter) or its approximation using skin/mucosa (e.g., axillary, oral, or rectal) temperature measurements. With recent advances in material science and technology, thermal measurements went beyond core body temperature measurements and found their way in many medical specialties. The article consists of two primary parts. In the first part we overviewed current clinical thermal measurement technologies across two dimensions: (a) direct vs. indirect and (b) single-point vs. multiple-point temperature measurements. In the second part, we focus primarily on clinical applications in wound care, surgery, and sports medicine. The primary focus here is the thermographic imaging modality. However, other thermal modalities are included where relevant for these clinical applications. The literature review identified two primary use scenarios for thermographic imaging: inflammation-based and perfusion-based. These scenarios rely on local (topical) temperature measurements, which are different from systemic (core body temperature) measurements. Quantifying these types of diseases benefits from thermographic imaging of an area in contrast to single-point measurements. The wide adoption of the technology would be accelerated by larger studies supporting the clinical utility of thermography.

DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning

Burn wounds are most commonly evaluated through visual inspection to determine surgical candidacy, taking into account burn depth and individualized patient factors. This process, though cost effective, is subjective and varies by provider experience. Deep learning models can assist in burn wound surgical candidacy with predictions based on the wound and patient characteristics. To this end, we present a multimodal deep learning approach and a complementary mobile application - DL4Burn - for predicting burn surgical candidacy, to emulate the multi-factored approach used by clinicians. Specifically, we propose a ResNet50-based multimodal model and validate it using retrospectively obtained patient burn images, demographic, and injury data.

Use of thermal imaging and a dedicated wound imaging smartphone app as an adjunct to staging hidradenitis suppurativa

Hidradenitis suppurativa (HS) presents with painful nodules, draining tunnels, abscesses, ulcers, and fistula formation¹ . Grading systems, (e.g. Hurley Staging System, International Hidradenitis Suppurativa Severity Score System (IHS4), Severity Assessment of Hidradenitis Suppurativa Score (HS-PGA score), and Hidradenitis Suppurativa Area and Severity Index (HASI)) assess disease severity in terms of lesion count, extension and morphology.

Potential for Machine Learning in Burn Care

INTRODUCTION

Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the field of burns management to optimise the care we deliver in the National Health Service (NHS) in the UK.

METHODS

Machine Learning methods which predict or classify are explored. This includes linear and logistic regression, artificial neural networks, deep learning, and decision tree analysis.

DISCUSSION

Utilizing Machine Learning in burns care holds potential from prevention, burns assessment, predicting mortality and critical care monitoring to healing time. Establishing a regional or national Machine Learning group would be the first step towards the development of these essential technologies.

CONCLUSION

The implementation of machine learning technologies will require buy-in from the NHS health boards, with significant implications with cost of investment, implementation, employment of machine learning teams and provision of training to medical professionals.

Artificial intelligence in the management and treatment of burns: a systematic review

BACKGROUND

Artificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies.

METHODS

A systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods.

RESULTS

A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively.

CONCLUSION

AI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.

A systematic review of machine learning and automation in burn wound evaluation: A promising but developing frontier

BACKGROUND

Visual evaluation is the most common method of evaluating burn wounds. Its subjective nature can lead to inaccurate diagnoses and inappropriate burn center referrals. Machine learning may provide an objective solution. The objective of this study is to summarize the literature on ML in burn wound evaluation.

METHODS

A systematic review of articles published between January 2000 and January 2021 was performed using PubMed and MEDLINE (OVID). Articles reporting on ML or automation to evaluate burn wounds were included. Keywords included burns, machine/deep learning, artificial intelligence, burn classification technology, and mobile applications. Data were extracted on study design, method of data acquisition, machine learning techniques, and machine learning accuracy.

RESULTS

Thirty articles were included. Nine studies used machine learning and automation to estimate percent total body surface area (%TBSA) burned, 4 calculated fluid estimations, 19 estimated burn depth, 5 estimated need for surgery, and 2 evaluated scarring. Models calculating %TBSA burned demonstrated accuracies comparable to or better than paper methods. Burn depth classification models achieved accuracies of >83%.

CONCLUSION

Machine learning provides an objective adjunct that may improve diagnostic accuracy in evaluating burn wound severity. Existing models remain in the early stages with future studies needed to assess their clinical feasibility.

Use of Infrared Thermography for Assessment of Burn Depth and Healing Potential: A Systematic Review

INTRODUCTION

Burn wound depth assessments are an important component of determining patient prognosis and making appropriate management decisions. Clinical appraisal of the burn wound by an experienced burn surgeon is standard of care but has limitations. IR thermography is a technology in burn care that can provide a non-invasive, quantitative method of evaluating burn wound depth. IR thermography utilizes a specialized camera that can capture the infrared emissivity of the skin, and the resulting images can be analyzed to determine burn depth and healing potential of a burn wound. Though IR thermography has great potential for burn wound assessment, its use for this has not been well documented. Thus, we have conducted a systematic review of the current use of IR thermography to assess burn depth and healing potential.

METHODS

A systematic review and meta-analysis of the literature was performed on PubMed and Google Scholar between June 2020-December 2020 using the following keywords: FLIR, FLIR ONE, thermography, forward looking infrared, thermal imaging + burn*, burn wound assessment, burn depth, burn wound depth, burn depth assessment, healing potential, burn healing potential. A meta-analysis was performed on the mean sensitivity and specificity of the ability of IR thermography for predicting healing potential. Inclusion criteria were articles investigating the use of IR thermography for burn wound assessments in adults and pediatric patients. Reviews and non-English articles were excluded.

RESULTS

A total of 19 articles were included in the final review. Statistically significant correlations were found between IR thermography and laser doppler imaging (LDI) in 4/4 clinical studies. A case report of a single patient found that IR thermography was more accurate than LDI for assessing burn depth. Five articles investigated the ability of IR thermography to predict healing time, with four reporting statistically significant results. Temperature differences between burnt and unburnt skin were found in 2/2 articles. IR thermography was compared to clinical assessment in five articles, with varying results regarding accuracy of clinical assessment compared to thermography. Mean sensitivity and specificity of the ability of IR thermography to determine healing potential <15 days was 44.5 and 98.8 respectively. Mean sensitivity and specificity of the ability of FLIR to determine healing potential <21 days was 51.2 and 77.9 respectively.

CONCLUSION

IR thermography is an accurate, simple, and cost-effective method of burn wound assessment. FLIR has been demonstrated to have significant correlations with other methods of assessing burns such as LDI and can be utilized to accurately assess burn depth and healing potential.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

INTRODUCTION

Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.

METHODS

EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.

RESULTS

The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).

CONCLUSIONS

ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

Diagnostic accuracy of infrared thermal imaging for detecting COVID-19 infection in minimally symptomatic patients

INTRODUCTION

Despite being widely used as a screening tool, a rigorous scientific evaluation of infrared thermography for the diagnosis of minimally symptomatic patients suspected of having COVID-19 infection has not been performed.

METHODS

A consecutive sample of 60 adult individuals with a history of close contact with COVID-19 infected individuals and mild respiratory symptoms for less than 7 days and 20 confirmed COVID-19 negative healthy volunteers were enrolled in the study. Infrared thermograms of the face were obtained with a mobile camera, and RT-PCR was used as the reference standard test to diagnose COVID-19 infection. Temperature values and distribution of the face of healthy volunteers and patients with and without COVID-19 infection were then compared.

RESULTS

Thirty-four patients had an RT-PCR confirmed diagnosis of COVID-19 and 26 had negative test results. The temperature asymmetry between the lacrimal caruncles and the forehead was significantly higher in COVID-19 positive individuals. Through a random forest analysis, a cut-off value of 0.55°C was found to discriminate with an 82% accuracy between patients with and without COVID-19 confirmed infection.

CONCLUSIONS

Among adults with a history of COVID-19 exposure and mild respiratory symptoms, a temperature asymmetry of ≥ 0.55°C between the lacrimal caruncle and the forehead is highly suggestive of COVID-19 infection. This finding questions the widespread use of the measurement of absolute temperature values of the forehead as a COVID-19 screening tool.

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.

Intrarater Reliability of Digital Thermography in Detecting Pin Site Infection: A Proof of Concept Study

Aim and objective

The purpose of this study was to explore the capability and Intrarater reliability of thermography in detecting pin site infection.

Materials and methods

This is an explorative proof of concept study. Clinical assessment of pin sites was performed by one examiner with the Modified Gordon Pin Infection Classification from grade 0 to 6. Thermography of the pin sites was performed with a FLIR C3 camera. The analysis of the thermographic images was done in the software FLIR Tools. The maximum skin temperature around the pin site and the maximum temperature for the whole thermographic picture were measured. An Intrarater agreement was established and test-retests were performed with different camera angles.

Results

Thirteen (four females, nine males) patients (age 9–72 years) were included. Indications for frames: Fracture (n=4), two deformity correction, one lengthening and six bone transport. Days from surgery to thermography ranged from 27 to 385 days. Overall, 231 pin sites were included. Eleven pin sites were diagnosed with early signs of infection: five grade 1, five grade 2 and one grade 3. Mean pin site temperature for each patient was calculated, varied between patients from 29.0°C to 35.4°C (mean 33.9°C). With 34°C as cut-off value for infection, sensitivity was 73%; specificity, 67%; positive predictive value, 10%; and negative predictive value, 98%. Intrarater agreement for thermography was ICC 0.85 (0.77–0.92). The temperature measured was influenced by the camera positioning in relation to the pin site with a variance of 0.2.

Conclusions

Measurements of pin site temperature using the hand-held FLIR C3 infrared camera was a reliable method and the temperature was related to infection grading.

Clinical significance

This study demonstrated that digital thermography with a hand-held camera might be used for monitoring the pin sites after operations to detect early infection.

How to cite this article

Rahbek O, Husum HC, Fridberg M, et al. Intrarater Reliability of Digital Thermography in Detecting Pin Site Infection: A Proof of Concept Study. Strategies Trauma Limb Reconstr 2021;16(1):1–7.

Thermographic camera in traumatology, diabetic foot and reconstructive procedures

Traumatic lacerations, burns and ulcerations are a common cause of admission in the plastic surgery wards. Clinical evaluation alone sometimes provides insufficient or even inaccurate information. Thermographic camera is a new tool that could provide additional information regarding skin vascularization, presence of inflammation or involvement of deep tissue. A prospective study was realized for assessing pre and postoperative status of patients with lacerations, trauma, burn and diabetic foot. Preoperative evaluation helped in assessing bone involvement, inflammation and infection in order to decide the necessity of surgery. Postoperative evaluation was useful in preventing and lowering the rate of complications. Thermographic camera could be a new helpful and non-invasive tool especially in emergency hospitals in order to assess rapidly and objectively wound status and to start if necessary, a surgical treatment.

The Promise of Smartphone Applications in the Remote Monitoring of Postsurgical Wounds: A Literature Review

OBJECTIVE

To review the clinical and scientific literature on remote monitoring and management of postsurgical wounds using smartphone applications (apps).

DATA SOURCES

MEDLINE, PubMed, EMBASE, and Cochrane libraries were searched for relevant articles on patients who received surgery and were monitored postdischarge via an app.

STUDY SELECTION

Articles were selected with the terms "mobile phones," "smartphones," "wounds," "monitor," and "patient preference."

DATA EXTRACTION

The authors found 276 review articles related to telemedicine in wound care. Investigators reviewed the titles and abstracts of the search results and selected 83 articles that were relevant to the remote monitoring of wounds using smartphone apps.

DATA SYNTHESIS

The topics explored in selected literature included smartphone app importance to telemedicine, benefits (medical and financial), app examples, and challenges in the context of wound monitoring and management. The authors identified several challenges and limitations that future studies in the field need to address.

CONCLUSIONS

Remote monitoring and management of wounds using smartphone apps is a valuable technique to enhance the quality of and access to healthcare. However, although some patients may prefer this technology, some lack technological competence, limiting telemedicine's applicability. In addition, issues remain with the reliable interpretation of data collected through apps.