Skin temperature and vascular attributes as early warning signs of pressure injury

A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury

Introduction: Precise classification has an important role in treatment of pressure injury (PI), while current machine-learning or deeplearning based methods of PI classification remain low accuracy. Methods: In this study, we developed a deeplearning based weighted feature fusion architecture for fine-grained classification, which combines a top-down and bottom-up pathway to fuse high-level semantic information and low-level detail representation. We validated it in our established database that consist of 1,519 images from multi-center clinical cohorts. ResNeXt was set as the backbone network. Results: We increased the accuracy of stage 3 PI from 60.3% to 76.2% by adding weighted feature pyramid network (wFPN). The accuracy for stage 1, 2, 4 PI were 0.870, 0.788, and 0.845 respectively. We found the overall accuracy, precision, recall, and F1-score of our network were 0.815, 0.808, 0.816, and 0.811 respectively. The area under the receiver operating characteristic curve was 0.940. Conclusions: Compared with current reported study, our network significantly increased the overall accuracy from 75% to 81.5% and showed great performance in predicting each stage. Upon further validation, our study will pave the path to the clinical application of our network in PI management.

Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers

As today's society ages, age-related diseases become more frequent. One very common but yet preventable disease is the development of pressure ulcers (PUs). PUs can occur if tissue is exposed to a long-lasting pressure load, e.g., lying on tissue without turning. The cure of PUs requires intensive care, especially for the elderly or people with preexisting conditions whose tissue needs longer healing times. The consequences are heavy suffering for the patient and extreme costs for the health care system. To avoid these consequences, our objective is to develop a pressure ulcer prophylaxis device. For that, we built a new sensor system able to monitor the pressure load and tissue vital signs in immediate local proximity at patient's predilection sites. In the clinical study, we found several indicators showing correlations between tissue perfusion and the risk of PU development, including strongly reduced SpO2 levels in body tissue prior to a diagnosed PU. Finally, we propose a prophylaxis system that allows for the prediction of PU developments in early stages before they become visible. This work is the first step in generating an effective system to warn patients or caregivers about developing PUs and taking appropriate preventative measures. Widespread application could reduce patient suffering and lead to substantial cost savings.

Accuracy of Thermographic Imaging in the Early Detection of Pressure Injury: A Systematic Review

OBJECTIVE

To verify the accuracy of thermographic images in the early detection of pressure injury (PI) in adult patients.

DATA SOURCES

Between March 2021 and May 2022, researchers searched 18 databases for relevant articles using nine keywords. In total, 755 studies were evaluated.

STUDY SELECTION

Eight studies were included in the review. Studies were included if they evaluated individuals older than 18 years who were admitted to any healthcare setting; were published in English, Spanish, or Portuguese; examined the accuracy of thermal imaging in the early detection of PI, including suspected stage 1 PI or deep tissue injury; and they compared the region of interest to another area or control group, or to the Braden Scale or Norton Scale. Animal studies and reviews, studies with contact infrared thermography, and those including stages 2, 3, 4, and unstageable PIs were excluded.

DATA EXTRACTION

Researchers examined sample characteristics and assessment measures related to image capture, including environmental, individual, and technical factors.

DATA SYNTHESIS

Across the included studies, sample sizes ranged from 67 to 349 participants, and patients were followed up for periods ranging from a single assessment up to 14 days, or until the appearance of a PI, discharge, or death. Evaluation with the infrared thermography identified temperature differentials between regions of interest and/or in comparison with risk assessment scales.

CONCLUSIONS

Evidence on the accuracy of thermographic imaging in the early detection of PI is limited.

Electronic Alert Signal for Early Detection of Tissue Injuries in Patients: An Innovative Pressure Sensor Mattress

Monitoring the early stage of developing tissue injuries requires intact skin for surface detection of cell damage. However, electronic alert signal for early detection is limited due to the lack of accurate pressure sensors for lightly pigmented skin injuries in patients. We developed an innovative pressure sensor mattress that produces an electronic alert signal for the early detection of tissue injuries. The electronic alert signal is developed using a web and mobile application for pressure sensor mattress reporting. The mattress is based on body distributions with reference points, temperature, and a humidity sensor to detect lightly pigmented skin injuries. Early detection of the pressure sensor is linked to an electronic alert signal at 32 mm Hg, a temperature of 37 °C, a relative humidity of 33.5%, a response time of 10 s, a loading time of 30 g, a density area of 1 mA, and a resistance of 7.05 MPa (54 N) at 0.87 m³/min. The development of the innovative pressure sensor mattress using an electronic alert signal is in line with its enhanced pressure detection, temperature, and humidity sensors.

Application of an infrared thermography-based model to detect pressure injuries: a prospective cohort study

BACKGROUND

It is challenging to detect pressure injuries at an early stage of their development.

OBJECTIVES

To assess the ability of an infrared thermography (IRT)-based model, constructed using a convolution neural network, to reliably detect pressure injuries.

METHODS

A prospective cohort study compared validity in patients with pressure injury (n = 58) and without pressure injury (n = 205) using different methods. Each patient was followed up for 10 days.

RESULTS

The optimal cut-off values of the IRT-based model were 0·53 for identifying tissue damage 1 day before visual detection of pressure injury and 0·88 for pressure injury detection on the day visual detection is possible. Kaplan-Meier curves and Cox proportional hazard regression model analysis showed that the risk of pressure injury increased 13-fold 1 day before visual detection with a cut-off value higher than 0·53 [hazard ratio (HR) 13·04, 95% confidence interval (CI) 6·32-26·91; P < 0·001]. The ability of the IRT-based model to detect pressure injuries [area under the receiver operating characteristic curve (AUC)_{lag 0 days} , 0·98, 95% CI 0·95-1·00] was better than that of other methods.

CONCLUSIONS

The IRT-based model is a useful and reliable method for clinical dermatologists and nurses to detect pressure injuries. It can objectively and accurately detect pressure injuries 1 day before visual detection and is therefore able to guide prevention earlier than would otherwise be possible. What is already known about this topic? Detection of pressure injuries at an early stage is challenging. Infrared thermography can be used for the physiological and anatomical evaluation of subcutaneous tissue abnormalities. A convolutional neural network is increasingly used in medical imaging analysis. What does this study add? The optimal cut-off values of the IRT-based model were 0·53 for identifying tissue damage 1 day before visual detection of pressure injury and 0·88 for pressure injury detection on the day visual detection is possible. Infrared thermography-based models can be used by clinical dermatologists and nurses to detect pressure injuries at an early stage objectively and accurately.

Pressure Injury Link to Entropy of Abdominal Temperature

This study examined the association between pressure injuries and complexity of abdominal temperature measured in residents of a nursing facility. The temperature served as a proxy measure for skin thermoregulation. Refined multiscale sample entropy and bubble entropy were used to measure the irregularity of the temperature time series measured over two days at 1-min intervals. Robust summary measures were derived for the multiscale entropies and used in predictive models for pressure injuries that were built with adaptive lasso regression and neural networks. Both types of entropies were lower in the group of participants with pressure injuries (n=11) relative to the group of non-injured participants (n=15). This was generally true at the longer temporal scales, with the effect peaking at scale τ=22 min for sample entropy and τ=23 min for bubble entropy. Predictive models for pressure injury on the basis of refined multiscale sample entropy and bubble entropy yielded 96% accuracy, outperforming predictions based on any single measure of entropy. Combining entropy measures with a widely used risk assessment score led to the best prediction accuracy. Complexity of the abdominal temperature series could therefore serve as an indicator of risk of pressure injury.

Quantifying skin sensitivity caused by mechanical insults: A review

BACKGROUND

Skin sensitivity (SS) is a commonly occurring response to a range of stimuli, including environmental conditions (e.g., sun exposure), chemical irritants (e.g., soaps and cosmetics), and mechanical forces (e.g., while shaving). From both industry and academia, many efforts have been taken to quantify the characteristics of SS in a standardised manner, but the study is hindered by the lack of an objective definition.

METHODS

A review of the scientific literature regarding different parameters attributed to the loss of skin integrity and linked with exhibition of SS was conducted. Articles included were screened for mechanical stimulation of the skin, with objective quantification of tissue responses using biophysical or imaging techniques. Additionally, studies where cohorts of SS and non-SS individuals were reported have been critiqued.

RESULTS

The findings identified that the structure and function of the stratum corneum and its effective barrier properties are closely associated with SS. Thus, an array of skin tissue responses has been selected for characterization of SS due to mechanical stimuli, including: transepidermal water loss, hydration, redness, temperature, and sebum index. Additionally, certain imaging tools allow quantification of the superficial skin layers, providing structural characteristics underlying SS.

CONCLUSION

This review proposes a multimodal approach for identification of SS, providing a means to characterise skin tissue responses objectively. Optical coherence tomography (OCT) has been suggested as a suitable tool for dermatological research with clinical applications. Such an approach would enhance the knowledge underlying the multifactorial nature of SS and aid the development of personalised solutions in medical and consumer devices.