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

2021 ◽  
Author(s):  
Justin Dang ◽  
Matthew Lin ◽  
Calvin Tan ◽  
Christopher H Pham ◽  
Samantha Huang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Clinical Assessment ◽  
Meta Analysis ◽  
Ir Thermography ◽  
Burn Wound ◽  
Burn Care ◽  
Wound Assessment ◽  
Burn Depth ◽  
Mean Sensitivity

Abstract 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.

558 Use of Forward-Looking Infrared (FLIR) Technology to Assess Burn Depth and Healing Potential: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S128-S129
Author(s):  
Justin Dang ◽  
Matthew E Lin ◽  
Samantha Huang ◽  
Ian F Hulsebos ◽  
Haig A Yenikomshian ◽  
...  
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Clinical Assessment ◽  
Meta Analysis ◽  
Burn Wound ◽  
Burn Care ◽  
Wound Assessment ◽  
Burn Depth ◽  
Mean Sensitivity ◽  
Forward Looking

Abstract 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. Forward-looking Infrared (FLIR) is a new technology in burn care that can provide a non-invasive, quantitative method of evaluating burn wound depth. FLIR 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 FLIR has great potential for burn wound assessment, its use for this has not been well documented. Thus, we have conducted a systematic review and meta-analysis of the current use of FLIR technology to assess burn depth and healing potential. Methods A systematic review of the literature was performed on PubMed and Google Scholar between June 2020-August 2020 using the following keywords: thermal imaging, FLIR, forward looking infrared, burn, burn depth. Meta-analysis was performed on the mean sensitivity and specificity of the ability of FLIR to predict healing potential. Inclusion criteria were articles investigating the use of FLIR for burn wound assessments in adults, pediatric patients and animal models. Reviews and non-English articles were excluded. Results A total of 11 articles were included in the final review. Statistically significant correlations were found between FLIR and laser doppler imaging (LDI) in 3/3 clinical studies. A case report of a single patient found that FLIR was more accurate than LDI for assessing burn depth. Three articles investigated the ability of FLIR to predict healing potential, with all three reporting statistically significant results. Significant temperature differences between burnt and unburnt skin were found in 2/2 articles. FLIR was compared to clinical assessment by burn surgeons in two articles; one article found that FLIR was more accurate for assessing burn depth, while the other article found that clinical assessment was more accurate for predicting healing potential < 21 days. Mean sensitivity and specificity of the ability of FLIR 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 44.0 and 77.4 respectively. Conclusions FLIR 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.

674 Machine Learning and Automation in Burn Care: A Systematic Review

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S193-S193
Author(s):  
Samantha Huang ◽  
Justin Dang ◽  
Clifford C Sheckter ◽  
Haig A Yenikomshian ◽  
Justin Gillenwater
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Total Body Surface Area ◽  
Burn Wound ◽  
Burn Care ◽  
Surgeon Experience ◽  
Automated Software ◽  
Burn Depth ◽  
Automated Methods ◽  
Equal Weighting

Abstract Introduction Current methods of burn evaluation and treatment are subjective and dependent on surgeon experience, with high rates of inter-rater variability leading to inaccurate diagnoses and treatment. Machine learning (ML) and automated methods are being used to develop more objective and accurate methods for burn diagnosis and triage. Defined as a subfield of artificial intelligence that applies algorithms capable of knowledge acquisition, machine learning draws patterns from data, which it can then apply to clinically relevant tasks. This technology has the potential to improve burn management by quantitating diagnoses, improving diagnostic accuracy, and increasing access to burn care. The aim of this systematic review is to summarize the literature regarding machine learning and automated methods for burn wound evaluation and treatment. Methods A systematic review of articles available on PubMed and MEDLINE (OVID) was performed. Keywords used in the search process included burns, machine learning, deep learning, burn classification technology, and mobile applications. Reviews, case reports, and opinion papers were excluded. Data were extracted on study design, study objectives, study models, devices used to capture data, machine learning, or automated software used, expertise level and number of evaluators, and ML accuracy of burn wound evaluation. Results The search identified 592 unique titles. After screening, 35 relevant articles were identified for systematic review. Nine studies used machine learning and automated software to estimate percent total body surface area (%TBSA) burned, 4 calculated fluid requirements, 18 estimated burn depth, 5 estimated need for surgery, 6 predicted mortality, and 2 evaluated scarring in burn patients. Devices used to estimate %TBSA burned showed an accuracy comparable to or better than traditional methods. Burn depth estimation sensitivities resulted in unweighted means >81%, which increased to >83% with equal weighting applied. Mortality prediction sensitivity had an unweighted mean of 96.75%, which increased to 99.35% with equal weighting. Conclusions Machine learning and automated technology are promising tools that provide objective and accurate measures of evaluating burn wounds. Existing methods address the key steps in burn care management; however, existing data reporting on their robustness remain in the early stages. Further resources should be dedicated to leveraging this technology to improve outcomes in burn care.

scholarly journals Impact of Virtual Reality Technology on Pain and Anxiety in Pediatric Burn Patients: A Systematic Review and Meta-Analysis

2022 ◽  
Vol 2 ◽  
Author(s):  
Kathryn L. Smith ◽  
Yang Wang ◽  
Luana Colloca
Keyword(s):  
Systematic Review ◽  
Virtual Reality ◽  
Effect Size ◽  
Pediatric Patients ◽  
Wound Care ◽  
Meta Analysis ◽  
Combined Effect ◽  
Burn Wound ◽  
Virtual Reality Technology ◽  
Burn Care

Introduction: Virtual reality (VR) has the potential to lessen pain and anxiety experienced by pediatric patients undergoing burn wound care procedures. Population-specific variables require novel technological application and thus, a systematic review among studies on its impact is warranted.Objective: The objective of this review was to evaluate the effectiveness of VR on pain in children with burn injuries undergoing wound care procedures.Methods: A systematic literature review was performed using PubMed and CINAHL databases from January 2010 to July 2021 with the keywords “pediatric,” “burn,” “virtual reality,” and “pain.” We included experimental studies of between- and within-subjects designs in which pediatric patients’ exposure to virtual reality technology during burn wound care functioned as the intervention of interest. Two researchers independently performed the literature search, made judgements of inclusion/exclusion based on agreed-upon criteria, abstracted data, and assessed quality of evidence using a standardized appraisal tool. A meta-analysis was conducted to evaluate the effectiveness of the VR on burning procedural pain in pediatric population. Standardized mean difference (SMD) was used as an index of combined effect size, and a random effect model was used for meta-analysis.Results: Ten articles published between January 2010 and July 2021 passed the selection criteria: six randomized controlled trials and four randomized repeated-measures studies. Consistent results among the studies provided support for VR as effective in reducing pain and potentially pain related anxiety in children undergoing burn wound care through preprocedural preparation (n = 2) and procedural intervention (n = 8). A random effects meta-analysis model indicated a moderate and significant combined effect size (SMD = 0.60, 95% CI = 0.28–0.93, p = 0.0031) of VR effects on pain intensity ratings with no significant heterogeneity of VR intervention effects between studies. Only one study reported direct influence of VR intervention on pre-procedural situational anxiety with a moderate effect size (Cohen’s d = 0.575, 95%CI = 0.11–1.04).Conclusion: Children’s exposure to VR during burn care procedures was associated with lower levels of pain and pain related anxiety. Moderate to large effect sizes support the integration of VR into traditional pediatric burn pain protocols irrespective of innovative delivery methods and content required for use in burned pediatric patients.

miR-224 is an early-stage biomarker of hepatocellular carcinoma with miR-224 and miR-125b as prognostic biomarkers

2020 ◽  
Vol 14 (15) ◽  
pp. 1485-1500
Author(s):  
Lichao Yang ◽  
Chunmeng Wei ◽  
Yasi Li ◽  
Xiao He ◽  
Min He
Keyword(s):  
Hepatocellular Carcinoma ◽  
Systematic Review ◽  
Survival Analysis ◽  
Sensitivity And Specificity ◽  
Poor Prognosis ◽  
Early Stage ◽  
Meta Analysis ◽  
Benign Lesion ◽  
Diagnostic Efficiency ◽  
Low Expression

Aim: The aim was to systematically investigate the miRNA biomarkers for early diagnosis of hepatocellular carcinoma (HCC). Materials & methods: A systematic review and meta-analysis of miRNA expression in HCC were performed. Results: A total of 4903 cases from 30 original studies were comprehensively analyzed. The sensitivity and specificity of miR-224 in discriminating early-stage HCC patients from benign lesion patients were 0.868 and 0.792, which were superior to α-fetoprotein. Combined miR-224 with α-fetoprotein, the sensitivity and specificity were increased to 0.882 and 0.808. Prognostic survival analysis showed low expression of miR-125b and high expression of miR-224 were associated with poor prognosis. Conclusion: miR-224 had a prominent diagnostic efficiency in early-stage HCC, with miR-224 and miR-125b being valuable in the prognostic diagnosis.

scholarly journals Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

2021 ◽  
Vol 10 (7) ◽  
pp. 1543
Author(s):  
Morwenn Le Boulc’h ◽  
Julia Gilhodes ◽  
Zara Steinmeyer ◽  
Sébastien Molière ◽  
Carole Mathelin
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
False Negative ◽  
Significant Proportion ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Axillary Staging ◽  
Positron Emission ◽  
Positive Rate

Background: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. Methods: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43–59%) and 100% (95% CI: 99–100%) for US, 83% (95% CI: 72–91%) and 85% (95% CI: 72–92%) for MRI, and 49% (95% CI: 39–59%) and 94% (95% CI: 91–96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. Conclusions: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

scholarly journals Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Kaiwen Qin ◽  
Jianmin Li ◽  
Yuxin Fang ◽  
Yuyuan Xu ◽  
Jiahao Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Wireless Capsule Endoscopy ◽  
Convolution Neural Network ◽  
Research Progress ◽  
Gi Bleeding ◽  
Wireless Capsule

Abstract Background Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE. Methods A search in PubMed, SinoMed, and Web of Science was conducted to collect all original publications about CNN implementation in WCE. Assessment of the risk of bias was performed by Quality Assessment of Diagnostic Accuracy Studies-2 risk list. Pooled sensitivity and specificity were calculated by an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. Results 16 articles with 23 independent studies were included. CNN application to WCE was divided into detection on erosion/ulcer, gastrointestinal bleeding (GI bleeding), and polyps/cancer. The pooled sensitivity of CNN for erosion/ulcer is 0.96 [95% CI 0.91, 0.98], for GI bleeding is 0.97 (95% CI 0.93–0.99), and for polyps/cancer is 0.97 (95% CI 0.82–0.99). The corresponding specificity of CNN for erosion/ulcer is 0.97 (95% CI 0.93–0.99), for GI bleeding is 1.00 (95% CI 0.99–1.00), and for polyps/cancer is 0.98 (95% CI 0.92–0.99). Conclusion Based on our meta-analysis, CNN-dependent diagnosis of erosion/ulcer, GI bleeding, and polyps/cancer approached a high-level performance because of its high sensitivity and specificity. Therefore, future perspective, CNN has the potential to become an important assistant for the diagnosis of WCE.

scholarly journals Genome-wide copy number variations as molecular diagnostic tool for cutaneous intermediate melanocytic lesions: a systematic review and individual patient data meta-analysis

2021 ◽  
Author(s):  
Chiel F. Ebbelaar ◽  
Anne M. L. Jansen ◽  
Lourens T. Bloem ◽  
Willeke A. M. Blokx
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Copy Number ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Copy Number Variations ◽  
Patient Data ◽  
Melanocytic Lesions ◽  
Genome Wide ◽  
Malignant Lesions

AbstractCutaneous intermediate melanocytic neoplasms with ambiguous histopathological features are diagnostically challenging. Ancillary cytogenetic techniques to detect genome-wide copy number variations (CNVs) might provide a valuable tool to allow accurate classification as benign (nevus) or malignant (melanoma). However, the CNV cut-off value to distinguish intermediate lesions from melanoma is not well defined. We performed a systematic review and individual patient data meta-analysis to evaluate the use of CNVs to classify intermediate melanocytic lesions. A total of 31 studies and 431 individual lesions were included. The CNV number in intermediate lesions (median 1, interquartile range [IQR] 0–2) was significantly higher (p<0.001) compared to that in benign lesions (median 0, IQR 0–1) and lower (p<0.001) compared to that in malignant lesions (median 6, IQR 4–11). The CNV number displayed excellent ability to differentiate between intermediate and malignant lesions (0.90, 95% CI 0.86–0.94, p<0.001). Two CNV cut-off points demonstrated a sensitivity and specificity higher than 80%. A cut-off of ≥3 CNVs corresponded to 85% sensitivity and 84% specificity, and a cut-off of ≥4 CNVs corresponded to 81% sensitivity and 91% specificity, respectively. This individual patient data meta-analysis provides a comprehensive overview of CNVs in cutaneous intermediate melanocytic lesions, based on the largest pooled cohort of ambiguous melanocytic neoplasms to date. Our meta-analysis suggests that a cut-off of ≥3 CNVs might represent the optimal trade-off between sensitivity and specificity in clinical practice to differentiate intermediate lesions from melanoma.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

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