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This scoping review investigates the use of point-of-care infrared thermography devices for assessing various wound types. A comprehensive search across four databases yielded 76 studies published between 2010 and 2024 that met the inclusion criteria. The review highlights thermography applications in burns, surgical wounds, diabetic foot ulcers, pressure injuries, and other lower limb wounds. Key findings indicate its effectiveness in detecting early signs of inflammation and healing delays, facilitating timely interventions. The technology shows promise in accurately predicting wound healing trajectories and assessing treatment outcomes. Recent advancements have made thermographic devices more affordable and user-friendly, expanding their clinical potential. However, challenges persist, including reimbursement, training requirements, and integration with electronic medical records (EMRs), with EMR integration identified as a critical barrier to widespread adoption. While preliminary findings are promising, the current evidence base is constrained by small sample sizes, retrospective study designs, and limited consideration of skin tone variability. Large, prospective studies are essential to validate the clinical utility of thermography in wound care and to inform the development of standardised protocols that support equitable, bias-reduced assessment across diverse populations. Addressing these gaps is critical for advancing research, enhancing clinician training, and improving patient outcomes in wound care. Overall, point-of-care thermography demonstrates significant potential to enhance wound assessment and monitoring, thereby elevating care quality and patient outcomes.
This study investigated the relationship between clinician assessments and the AI-generated scores, highlighting how correlations vary based on clinician expertise. It also explored the proportion of tissue types identified by clinicians relative to AI assessments and assess the inter-clinician agreement in quantifying tissue types, identifying variations based on clinician experience. A cross-sectional survey used purposive, non-random sampling to recruit 50 wound care clinicians. Participants reported their specialisation and experience level before identifying and quantifying granulation, slough, eschar, and epithelialisation in nine wound images. An AI model analysed the same images for comparison. Experienced clinicians and wound care specialists reported higher confidence in assessments. Inter-clinician agreement was moderate–good for granulation and slough (ICC: 0.763–0.762) and moderate–excellent for eschar (ICC: 0.910), but moderate–poor for epithelialisation (ICC: 0.435). Clinicians strongly correlated with AI for granulation, slough, and eschar (r = 0.879, 0.955 and 0.984, respectively). Epithelialisation was more challenging, with a 60% identification rate and moderate correlation with AI (r = 0.579). AI-generated scores aligned with clinician assessments for granulation, slough, and eschar. However, epithelialisation, which is crucial for objectively measuring healing progress, showed greater variability, suggesting that AI could improve the reliability of its assessment, potentially leading to more consistent wound evaluation to guide treatment decisions.
This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real-world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in-house pressure injuries was conducted using a large calibrated wound database. This study employed a time-varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries. Feature selection was executed through a forward-backward process to identify significant predictive factors. The study found that sensory and moisture Braden subscores were minimally contributive and were consequently discarded. The most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The model demonstrated a 10.4% increase in predictive accuracy compared with traditional Braden scores, indicating a significant improvement. The study suggests that disaggregating Braden scores and incorporating detailed wound histories and demographic data can substantially enhance the accuracy of pressure injury risk assessments in SNFs. This approach aligns with the evolving trend towards more personalized and detailed patient care. These findings propose a new direction in pressure injury risk assessment, potentially leading to more effective and individualized care strategies in SNFs. The study highlights the value of large-scale data in wound care, suggesting its potential to enhance quantitative approaches for pressure injury risk assessment and supporting more accurate, data-driven clinical decision-making.
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