Is thermography a viable solution for detecting pressure injuries in dark skin patients?

Andrew Chung; Joyce C. Ho; Kathleen Jordan; Miriam Asare-Baiden; Sharon Eve Sonenblum

REVIEW 3 major objections 5 minor 31 references

Is thermography a viable solution for detecting pressure injuries in dark skin patients?

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A CNN trained on thermal images detects temperature changes and erythema in dark skin more reliably than optical images, with perfect cooling-task scores and an AUC above 0.93 for erythema.

desk verdict Genuinely useful new dataset, but the image-level split invalidates the headline AUC/F1; needs participant-level evaluation before the robustness claim can stand. read the letter →

arxiv 2411.10627 v1 pith:N3X35YYF submitted 2024-11-15 cs.CV cs.AI

Miriam Asare-Baiden , Kathleen Jordan , Andrew Chung , Sharon Eve Sonenblum , Joyce C. Ho This is my paper · ORCID

classification cs.CVcs.AI

keywords pressureinjurydetectionthermographythermalimagingdarkskintoneserythemaMobileNetV2convolutionalneuralnetworkimageclassification

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests whether thermography can replace visual inspection for early pressure-injury detection in dark skin, where redness is hard to see. The authors collected a new dataset of 35 participants, predominantly with dark skin tones, and induced temperature changes and erythema on the lower back under varied imaging protocols. They trained a small convolutional neural network on thermal and optical images for two binary tasks, cooling detection and erythema detection. The thermal-trained model achieved perfect scores on the cooling task and outperformed the optical model on erythema, suggesting that thermography is a viable, pigment-independent route to pressure-injury screening.

What carries the argument

The central object is MobileNetV2, a compact convolutional neural network with 19 residual bottleneck layers, fine-tuned from ImageNet pretraining with a fixed learning rate and Adam optimizer. The argument runs through the dataset: thermal and optical images of the lower back from 35 healthy adults spanning four Eumelanin skin-tone categories, with temperature changes induced by a cooled stone and a cupping device, and acquisition varied by camera, lighting, distance, and pose. Thermal images carry the temperature difference directly, whereas optical images carry color that melanin masks; the network is trained separately on optical, grayscale thermal, and color thermal images so the two representations can be compared on identical binary classification tasks and test splits.

What would settle it

Re-run the same MobileNetV2 pipeline with a participant-level split so that all images from one person are kept in either training or testing, and compare the cooling and erythema AUC and F1 to Table 1; if the thermal advantage shrinks or the absolute scores fall substantially, the reported robustness is an artifact of image-level leakage.

Watch

Extended reading notes

Core claim

The central claim is that a thermography-based CNN is robust to data collection protocols for all skin tones and detects induced temperature changes and erythema in darker skin more reliably than optical images. On the cooling task, MobileNetV2 reaches an AUC and F1 of 1.0 with both grayscale and color thermal images, versus 0.818 and 0.711 with optical images. On the erythema task, the thermal models achieve an F1 of at least 0.914 and an AUC of at least 0.935, compared with 0.868 and 0.909 for optical images. The study interprets this as evidence that temperature-based imaging bypasses the melanin-related visual masking that makes visual inspection unreliable, and that strict imaging protocols may not be necessary for thermal acquisition.

Load-bearing premise

The claim rests on treating every image as an independent sample, even though dozens of images come from each participant, so the same person's images appear in both the training and test sets and the model could be recognizing participants rather than temperature changes.

Editorial extensions

If this is right

Clinical adoption of thermography for pressure-injury screening would not require strict control of lighting, camera distance, or patient pose, since thermal model performance stayed high across protocol variations.
Dark-skin patients, who are currently at higher risk of missed early pressure injuries, could be screened with thermal imaging instead of relying on visual redness.
A compact network like MobileNetV2 is light enough for bedside or portable devices, making real-time thermographic screening feasible in routine nursing workflows.
Thermal imaging may serve as a more reliable early-warning signal than optical imaging for erythema, the first visible sign of pressure injury, across intermediate and high eumelanin skin tones.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A participant-level (stratified) train/test split is the natural next test: because the current split is random at the image level, images from the same participant appear in both training and testing, so the model may have learned participant identity rather than generalizable temperature patterns.
The erythema result is based on a single imaging protocol, so the claim of protocol robustness applies mainly to the cooling task and should be re-tested for erythema under varied lighting and distance.
If a patient-level split erases most of the thermal-versus-optical gap, the practical conclusion would shift from thermography being categorically better to both modalities performing similarly once identity leakage is removed, which would change the clinical recommendation.
A natural extension is to test on real pressure injuries rather than induced temperature changes, since real injuries involve tissue damage, moisture, and chronic inflammation that may alter thermal signatures differently than cooling or cupping.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things you should know. First, the dataset is a real contribution: 35 participants, mostly dark skin, thermal and optical images with controlled variations in lighting, distance, pose, and camera, plus two tasks (cooling and cupping-induced erythema). Second, the central claim—that a thermography-based CNN is robust to collection protocol across skin tones—is not supported by the evaluation as written, because the random image-level split lets images from the same participant appear in both train and test.

What the paper does well: it addresses an equity-relevant gap, it gathers a new dataset rather than reusing an old one, and it compares thermal vs optical imaging head-to-head on the same data. The protocol variation is thoughtful and the proxy design (induced temperature change and erythema) is a reasonable first step. The authors are transparent about the small sample and simulated setting.

The soft spot is the split. Each cooling participant contributes 48 images; each erythema participant contributes 9. An 80/20 random image split will put the same person on both sides. With 35 participants and a powerful model like MobileNetV2, the model can exploit participant identity—background, stone placement, skin texture, cupping marks—rather than the signal of temperature change. The perfect AUC/F1 on cooling and near-perfect erythema numbers therefore tell you little about generalization to a new patient. This is not a small quibble; it is the statistical basis for every headline number. The paper's limitations section lists sample size and simulated conditions but never mentions this confounding. Also missing: confidence intervals, a participant-level split, and any plan to release the data or code. The erythema positive threshold (6 c.u.) is a free parameter, but that is minor compared with the leakage.

My take: the dataset is worth keeping, and the question deserves serious work, but the current results do not demonstrate what the abstract claims. A corrected analysis with a participant-level split, confidence intervals, and ideally real PI images (or at least a clearer argument for why induced erythema transfers) could change the verdict. I would send this to peer review, but I would want the authors to redo the evaluation before publication. If the journal is willing to accept a revised version, this becomes a useful preliminary study; as is, it overclaims.

Referee Report

3 major / 5 minor

Summary. The paper presents a new dataset of thermal and optical images from 35 healthy adults, predominantly with darker skin tones, collected under a cooling protocol and a cupping (erythema) protocol with deliberately varied imaging conditions (camera, lighting, distance, posture). The authors fine-tune MobileNetV2 on three image modalities (optical, grayscale thermal, and color thermal) for two binary classification tasks: cooling versus control and erythema-positive versus control. They report that thermal images achieve perfect or near-perfect AUC/F1 on both tasks, while optical images perform worse, and they conclude that thermography-based CNNs are robust to data collection protocols and may be viable for pressure injury detection in dark skin. The central claim rests entirely on the test-set metrics in Table 1.

Significance. If the reported results were statistically valid, the paper would provide an important, clinically relevant comparison of thermal versus optical imaging for early pressure injury detection across darker skin tones, where visual inspection is known to be unreliable. The dataset itself, with its multi-protocol collection focused on darker skin tones, is a useful contribution. However, the manuscript's central quantitative claims are undermined by a fundamental evaluation-design flaw (participant leakage), so the significance of the current results is not established. The paper is appropriately framed as preliminary, but the evaluation methodology does not support even a preliminary conclusion about generalization to unseen patients.

major comments (3)

[Section 2.2, Appendix B] The random image-level 80/20 split creates participant leakage. Each cooling participant contributes 48 images (2 cameras × 2 lighting conditions × 2 distances × 3 postures × 2 control/cool) and each erythema participant contributes 9 images. With a random split, images from the same participant appear in both training and test sets. MobileNetV2 can exploit participant-identifying cues (skin texture, background, stone placement, cupping marks, or the exact positioning of the stone) rather than learning the actual temperature or erythema signal. Consequently, the perfect AUC/F1 of 1.0 for the cooling task and the near-perfect scores for the erythema task in Table 1 cannot be interpreted as evidence of generalization to unseen patients. The authors must re-run the evaluation with a participant-level split (e.g., leave-participants-out or grouped k-fold) and report metrics with confidence intervals. This is the load-bearing issue for the paper's central claim.
[Section 2.2 and Section 2.3 (Impact of Image Protocol)] The abstract and conclusion claim that the thermography-based CNN is 'robust to data collection protocols for all skin tones,' but no per-protocol or per-skin-tone statistical analysis is provided for thermal images. Table 1 gives only aggregate test-set metrics, and the protocol-level analysis in Figure 2 is restricted to misclassified optical images. The robustness claim is therefore not directly supported by the evidence; even setting aside the leakage problem, the paper does not show that the thermal model's performance is invariant across lighting, distance, and posture. Please report thermal performance stratified by each protocol factor (or a proper interaction analysis) before claiming protocol robustness.
[Table 2 and Section 2.3] The per-skin-tone analysis for erythema (Table 2) consists of raw correct/incorrect counts from a single test split, with many skin-tone categories containing only 3–6 participants (Appendix A, Table 3). With this sample size, the counts cannot support the conclusion that thermography is more reliable than optical imaging 'for all skin tones' or that the InterMid category is particularly challenging. The manuscript should either provide confidence intervals or a more appropriate statistical comparison (e.g., bootstrap or mixed-effects model) and should temper the claim to reflect the exploratory nature of the results.

minor comments (5)

[Section 2.2] The section header 'T ask Classification' contains a spacing typo; it should read 'Task Classification.'
[Appendix A] The text uses 'lightning' where 'lighting' is intended (e.g., 'Variations in lightning, distance, cameras, and posture').
[Table 3 and Figure 4] The eczema skin-tone category labels are capitalized inconsistently (e.g., 'Intermediate Mid' versus 'InterMid'), and Figure 4 is referenced but not described in the main text; please align terminology and refer to the figure explicitly.
[Appendix B] The hyperparameter section states a fixed learning rate of 1 × 10^-3 but does not report the range considered in tuning; please clarify whether the learning rate was tuned or fixed.
[Appendix C] The confusion matrix for the cooling task reports 174 true negatives and 169 true positives, but the test set size is stated as 343 in Table 4; this sums to 343, but the breakdown by class should be stated explicitly for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: this is a supervised benchmark with an independent test split, and the reported metrics are direct empirical evaluations.

full rationale

This paper makes no derived prediction from first principles; it reports a supervised classification benchmark on a newly collected thermal and optical dataset. MobileNetV2 is trained on an 80/20 random image split and evaluated on held-out test images, so the AUC and F1 values in Table 1 are direct measurements rather than quantities forced by construction. No fitted parameter is later renamed as a prediction, and no equation defines an output in terms of its own target. The few same-author citations, such as Bates-Jensen et al. 2024, provide background or contextual support about the weak correlation between erythema index and temperature change; they are not load-bearing for the central thermal-versus-optical comparison, and no uniqueness or ansatz is imported from prior work. The random image-level split may permit participant identity leakage and is a legitimate statistical validity concern, but that is an evaluation soundness issue, not a circularity issue. The central claim that thermal imaging outperforms optical imaging is therefore self-contained with respect to circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central empirical claims depend on four domain assumptions: the proxy validity of induced temperature changes, the statistical independence of images from the same participant (violated by the design), the transferability of ImageNet features to thermal images, and the validity of the erythema-index ground truth. No new physical entities are introduced.

free parameters (3)

Erythema positive threshold = 6 c.u. above baseline
Defined in Appendix A; determines all erythema labels and therefore every erythema accuracy number. Chosen by the authors, not derived or validated against a clinical outcome.
Training hyperparameters = learning rate 1e-3, batch size 32, 50 epochs, early stopping patience 10
Hand-chosen in Appendix B; these standard hyperparameters affect all model outputs but are not the central scientific claim.
Data augmentation choices = horizontal flip or 20-degree rotation
Chosen by hand in Section 2.2; they shape what invariances the model learns.

assumptions (4)

domain assumption Induced cooling and cupping temperature changes on healthy lower backs are a valid proxy for pressure injury-related temperature changes.
Stated in Section 2.1: 'The controlled setup allows us to isolate temperature changes and assess whether thermography can reliably detect such changes, thus serving as a proxy for early PI detection.' If false, the study does not actually test PI detection.
domain assumption Images from the same participant are independent and can be randomly split across train and test.
Implicit in the random image-level split in Section 2.2. Images from the same person appear in both sets, so this assumption is violated, causing leakage that can inflate performance.
domain assumption MobileNetV2 initialized with ImageNet weights transfers to thermal and optical medical images.
Used throughout Section 2.2; no feature-analysis or transfer study is provided for the thermal modality, and thermal images differ substantially from natural images.
domain assumption The erythema index measured by a colorimeter is a valid ground truth for erythema, and the 6 c.u. threshold marks clinically meaningful erythema.
Used to label all erythema images in Appendix A; the paper cites a related study (Bates-Jensen et al., 2024) finding weak correlations between erythema index and temperature changes, which weakens this assumption.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Is thermography a viable solution for detecting pressure injuries in dark skin patients?." pith.science (2026). https://pith.science/paper/N3X35YYF

@misc{pith2026241110627,
  author       = {Pith},
  title        = {Pith review of: Is thermography a viable solution for detecting pressure injuries in dark skin patients?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N3X35YYF}},
  note         = {Machine review of arXiv:2411.10627}
}

read the original abstract

Pressure injury (PI) detection is challenging, especially in dark skin tones, due to the unreliability of visual inspection. Thermography has been suggested as a viable alternative as temperature differences in the skin can indicate impending tissue damage. Although deep learning models have demonstrated considerable promise toward reliably detecting PI, the existing work fails to evaluate the performance on darker skin tones and varying data collection protocols. In this paper, we introduce a new thermal and optical imaging dataset of 35 participants focused on darker skin tones where temperature differences are induced through cooling and cupping protocols. We vary the image collection process to include different cameras, lighting, patient pose, and camera distance. We compare the performance of a small convolutional neural network (CNN) trained on either the thermal or the optical images on all skin tones. Our preliminary results suggest that thermography-based CNN is robust to data collection protocols for all skin tones.

Figures

Figures reproduced from arXiv: 2411.10627 by the authors.

**Figure 2.** Bar plot of misclassified optical images based on image collection protocol. Impact of Skin Tone for Erythema Detection To better understand which skin tone categories are most susceptible to misclassification for the 3 image types, we further investigated the performance for each of the four skin tone categories [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Progression of erythema in thermal image from 0 − 7 minutes post-cupping. PC denotes Post-Cupping, square represents the cooling spot, circle represents the erythema spot. Appendix A. Dataset Details Cupping was used to induce erythema. A total of 9 images, consisting of 1 control image and 8 other images taken from 0 to 7 minutes after cupping were captured per patient. At each timeframe, the erythema index was t… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Workflow for classifying cooling or erythema images. for training are (a) batch size of 32 (b) 50 epochs (c) learning rate of 1 × e −3 and (d) early stopping (patience of 10). Adam optimizer (Kingma et al., 2020) and binary-cross entropy loss function were used. The …

**Figure 5.** Figure 5: Confusion matrix of the three image modalities in the cooling classification task. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: Confusion matrix of the three imaging modalities used in the erythema classification task. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

**Figure 7.** Figure 7: Eumelanin categories of misclassified optical cooling images. C.1. Additional Image Protocol Analysis We also assessed the protocols used in the data collection process to identify which will potentially be useful for detecting PI. We analyzed the misclassified opti…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 26 canonical work pages

[1]

Evaluation of infrared technology to detect category i and suspected deep tissue injury in hospitalised patients

Fazila Abu Bakar Aloweni, Shin Yuh Ang, Yee Yee Chang, Xin Ping Ng, Kai Yunn Teo, Andrea Chaeu Lin Choh, Ivy Hui Qi Goh, and Siew Hoon Lim. Evaluation of infrared technology to detect category i and suspected deep tissue injury in hospitalised patients. Journal of Wound Care, 28 0 (Sup12): 0 S9--S16, 2019

work page 2019
[2]

Efficacy and safety of neuromuscular electrical stimulation in the prevention of pressure injuries in critically ill patients: a randomized controlled trial

Miriam Viviane Baron, Paulo Eug \^e nio Silva, Janine Koepp, Janete de Souza Urbanetto, Andres Felipe Mantilla Santamaria, Michele Paula Dos Santos, Marcus Vinicius de Mello Pinto, Cristine Brandenburg, Isabel Cristina Reinheimer, Sonia Carvalho, Mário Bernardes Wagner, Thomas Miliou, Carlos Eduardo Poli de Figueiredo, and Bartira Ercília Pinheiro da Cost...

work page 2022
[3]

Accuracy of thermographic imaging in the early detection of pressure injury: a systematic review

Miriam Viviane Baron, Paulo Ricardo Hernandes Martins, Cristine Brandenburg, Janine Koepp, Isabel Cristina Reinheimer, Amanda Corr \^e a Dos Santos, Michele Paula Dos Santos, Andres Felipe Mantilla Santamaria, Thomas Miliou, and Bartira Erc \' lia Pinheiro da Costa. Accuracy of thermographic imaging in the early detection of pressure injury: a systematic ...

work page 2023
[4]

Thermal measurement of erythema across skin tones: Implications for clinical identification of early pressure injury

Barbara M Bates-Jensen, Kathleen Jordan, William Jewell, and Sharon E Sonenblum. Thermal measurement of erythema across skin tones: Implications for clinical identification of early pressure injury. Journal of Tissue Viability, 2024

work page 2024
[5]

Pressure ulcers and prevention among acute care hospitals in the united states

Sandra Bergquist-Beringer, Lei Dong, Jianghua He, and Nancy Dunton. Pressure ulcers and prevention among acute care hospitals in the united states. The Joint Commission Journal on Quality and Patient Safety, 39 0 (9): 0 404--414, 2013

work page 2013
[6]

The braden scale for predicting pressure sore risk

Nancy Bergstrom et al. The braden scale for predicting pressure sore risk. Nurs res, 36 0 (4): 0 205--210, 1987

work page 1987
[7]

Racial differences in detection of fever using temporal vs oral temperature measurements in hospitalized patients

Sivasubramanium V Bhavani, Zanthia Wiley, Philip A Verhoef, Craig M Coopersmith, and Ighovwerha Ofotokun. Racial differences in detection of fever using temporal vs oral temperature measurements in hospitalized patients. Jama, 328 0 (9): 0 885--886, 2022

work page 2022
[8]

Current perspectives on pressure injuries in persons with dark skin tones from the national pressure injury advisory panel

Joyce Black, Jill Cox, Virginia Capasso, Donna Z Bliss, Barbara Delmore, Vignesh Iyer, Jacqueline Massaro, Cassendra Munro, Joyce Pittman, and Elizabeth A Ayello. Current perspectives on pressure injuries in persons with dark skin tones from the national pressure injury advisory panel. Advances in Skin & Wound Care, 36 0 (9): 0 470--480, 2023

work page 2023

Show all 31 references

[9]

Evaluation of the pressure ulcers risk scales with critically ill patients: a prospective cohort study

Andressa Tomazini Borghardt, Thiago Nascimento do Prado, Thiago Moura de Ara \'u jo, Noemi Marisa Brunet Rogenski, and Maria Edla de Oliveira Bringuente. Evaluation of the pressure ulcers risk scales with critically ill patients: a prospective cohort study. Revista latino-amer...

2015
[10]

The opencv library

Gary Bradski. The opencv library. Dr. Dobb's Journal of Software Tools, 25: 0 120--125, 2000

2000
[11]

The eumelanin human skin colour scale: a proof-of-concept study

Ophelia E Dadzie, Rick A Sturm, Damilola Fajuyigbe, Antoine Petit, and Nina G Jablonski. The eumelanin human skin colour scale: a proof-of-concept study. British Journal of Dermatology, 187 0 (1): 0 99--104, 2022

2022
[12]

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 0 (7639): 0 115--118, 2017

2017
[13]

Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice Guideline

European Pressure Ulcer Advisory Panel , National Pressure Injury Advisory Panel , and Pan Pacific Pressure Injury Alliance . Prevention and Treatment of Pressure Ulcers/Injuries: Clinical Practice Guideline. EPUAP/NPIAP/PPPIA, 3rd ed. edition, 2019. Available from: http://www...

2019
[14]

Pressure ulcer categorization and reporting in domiciliary settings using deep learning and mobile devices: A clinical trial to evaluate end-to-end performance

Paul Fergus, Carl Chalmers, William Henderson, Danny Roberts, and Atif Waraich. Pressure ulcer categorization and reporting in domiciliary settings using deep learning and mobile devices: A clinical trial to evaluate end-to-end performance. IEEE Access, 11: 0 65138--65152, 2023

2023
[15]

Classification between pedestrians and motorcycles using fasterrcnn inception and ssd mobilenetv2

Nemuel Norman F Giron, Robert Kerwin C Billones, Alexis M Fillone, Jay Robert Del Rosario, Argel A Bandala, and Elmer P Dadios. Classification between pedestrians and motorcycles using fasterrcnn inception and ssd mobilenetv2. In 2020 IEEE 12th international conference on huma...

2020
[16]

Assessing the feasibility of subepidermal moisture to predict erythema and stage 1 pressure ulcers in persons with spinal cord injury: a pilot study

Marylou Guihan, Barbara M Bates-Jenson, Sophia Chun, Rama Parachuri, Amy S Chin, and Heather McCreath. Assessing the feasibility of subepidermal moisture to predict erythema and stage 1 pressure ulcers in persons with spinal cord injury: a pilot study. The journal of spinal co...

2012
[17]

Array programming with numpy

Charles R Harris, K Jarrod Millman, St \'e fan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al. Array programming with numpy. Nature, 585 0 (7825): 0 357--362, 2020

2020
[18]

Searching for mobilenetv3

Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al. Searching for mobilenetv3. Proceedings of the IEEE/CVF international conference on computer vision, pages 1314--1324, 2019

2019
[19]

Matplotlib: A 2d graphics environment

John D Hunter. Matplotlib: A 2d graphics environment. Computing in science & engineering, 9 0 (3): 0 90--95, 2007

2007
[20]

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

Xiaoqiong Jiang, Xiangqing Hou, Ning Dong, Haisong Deng, Yu Wang, Xiangwei Ling, Hailei Guo, Liping Zhang, and Fuman Cai. Skin temperature and vascular attributes as early warning signs of pressure injury. Journal of Tissue Viability, 29 0 (4): 0 258--263, 2020

2020
[21]

Determinants of mortality among older adults with pressure ulcers

Hui Min Khor, Juan Tan, Nor Izzati Saedon, Shahrul B Kamaruzzaman, Ai Vyrn Chin, Philip JH Poi, and Maw Pin Tan. Determinants of mortality among older adults with pressure ulcers. Archives of gerontology and geriatrics, 59 0 (3): 0 536--541, 2014

2014
[22]

A method for stochastic optimization

Diederik P Kingma, J Adam Ba, and J Adam. A method for stochastic optimization. arxiv 2014. arXiv preprint arXiv:1412.6980, 106: 0 6, 2020

2014 arXiv
[23]

Global prevalence and incidence of pressure injuries in hospitalised adult patients: A systematic review and meta-analysis

Zhaoyu Li, Frances Lin, Lukman Thalib, and Wendy Chaboyer. Global prevalence and incidence of pressure injuries in hospitalised adult patients: A systematic review and meta-analysis . International Journal of Nursing Studies, 105: 0 103546, 2020

2020
[24]

A deep learning approach for automated detection and segmentation of pressure ulcers using infrared-based thermal imaging

Bhaskar Pandey, Deepak Joshi, Ajat Shatru Arora, Nekram Upadhyay, and HS Chhabra. A deep learning approach for automated detection and segmentation of pressure ulcers using infrared-based thermal imaging. IEEE Sensors Journal, 22 0 (15): 0 14762--14768, 2022

2022
[25]

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019

2019
[26]

Imagenet large scale visual recognition challenge

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115: 0 211--252, 2015

2015
[27]

Mobilenetv2: Inverted residuals and linear bottlenecks

Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510--4520, 2018

2018
[28]

Nonblanchable erythema for predicting pressure ulcer development: a systematic review with an individual participant data meta-analysis

C Shi, LJ Bonnett, JC Dumville, and N Cullum. Nonblanchable erythema for predicting pressure ulcer development: a systematic review with an individual participant data meta-analysis. British Journal of Dermatology, 182 0 (2): 0 278--286, 2020

2020
[29]

Using technology to detect erythema across skin tones

Sharon Eve Sonenblum, Rahee Patel, Sarah Phrasavath, Sharon Xu, and Barbara M Bates-Jensen. Using technology to detect erythema across skin tones. Advances in Skin & Wound Care, 36 0 (10): 0 524--533, 2023

2023
[30]

Analysis of localized erythema using clinical indicators and spectroscopy

Stephen Sprigle, Maureen Linden, and Brian Riordan. Analysis of localized erythema using clinical indicators and spectroscopy. Ostomy/wound management, 49 0 (3): 0 42--52, 2003

2003
[31]

Infrared thermal images classification for pressure injury prevention incorporating the convolutional neural networks

Yu Wang, Xiaoqiong Jiang, Kangyuan Yu, Fuqian Shi, Longjiang Qin, Hui Zhou, and Fuman Cai. Infrared thermal images classification for pressure injury prevention incorporating the convolutional neural networks. IEEE Access, 9: 0 15181--15190, 2021

2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Evaluation of infrared technology to detect category i and suspected deep tissue injury in hospitalised patients

Fazila Abu Bakar Aloweni, Shin Yuh Ang, Yee Yee Chang, Xin Ping Ng, Kai Yunn Teo, Andrea Chaeu Lin Choh, Ivy Hui Qi Goh, and Siew Hoon Lim. Evaluation of infrared technology to detect category i and suspected deep tissue injury in hospitalised patients. Journal of Wound Care, 28 0 (Sup12): 0 S9--S16, 2019

work page 2019

[2] [2]

Efficacy and safety of neuromuscular electrical stimulation in the prevention of pressure injuries in critically ill patients: a randomized controlled trial

Miriam Viviane Baron, Paulo Eug \^e nio Silva, Janine Koepp, Janete de Souza Urbanetto, Andres Felipe Mantilla Santamaria, Michele Paula Dos Santos, Marcus Vinicius de Mello Pinto, Cristine Brandenburg, Isabel Cristina Reinheimer, Sonia Carvalho, Mário Bernardes Wagner, Thomas Miliou, Carlos Eduardo Poli de Figueiredo, and Bartira Ercília Pinheiro da Cost...

work page 2022

[3] [3]

Accuracy of thermographic imaging in the early detection of pressure injury: a systematic review

Miriam Viviane Baron, Paulo Ricardo Hernandes Martins, Cristine Brandenburg, Janine Koepp, Isabel Cristina Reinheimer, Amanda Corr \^e a Dos Santos, Michele Paula Dos Santos, Andres Felipe Mantilla Santamaria, Thomas Miliou, and Bartira Erc \' lia Pinheiro da Costa. Accuracy of thermographic imaging in the early detection of pressure injury: a systematic ...

work page 2023

[4] [4]

Thermal measurement of erythema across skin tones: Implications for clinical identification of early pressure injury

Barbara M Bates-Jensen, Kathleen Jordan, William Jewell, and Sharon E Sonenblum. Thermal measurement of erythema across skin tones: Implications for clinical identification of early pressure injury. Journal of Tissue Viability, 2024

work page 2024

[5] [5]

Pressure ulcers and prevention among acute care hospitals in the united states

Sandra Bergquist-Beringer, Lei Dong, Jianghua He, and Nancy Dunton. Pressure ulcers and prevention among acute care hospitals in the united states. The Joint Commission Journal on Quality and Patient Safety, 39 0 (9): 0 404--414, 2013

work page 2013

[6] [6]

The braden scale for predicting pressure sore risk

Nancy Bergstrom et al. The braden scale for predicting pressure sore risk. Nurs res, 36 0 (4): 0 205--210, 1987

work page 1987

[7] [7]

Racial differences in detection of fever using temporal vs oral temperature measurements in hospitalized patients

Sivasubramanium V Bhavani, Zanthia Wiley, Philip A Verhoef, Craig M Coopersmith, and Ighovwerha Ofotokun. Racial differences in detection of fever using temporal vs oral temperature measurements in hospitalized patients. Jama, 328 0 (9): 0 885--886, 2022

work page 2022

[8] [8]

Current perspectives on pressure injuries in persons with dark skin tones from the national pressure injury advisory panel

Joyce Black, Jill Cox, Virginia Capasso, Donna Z Bliss, Barbara Delmore, Vignesh Iyer, Jacqueline Massaro, Cassendra Munro, Joyce Pittman, and Elizabeth A Ayello. Current perspectives on pressure injuries in persons with dark skin tones from the national pressure injury advisory panel. Advances in Skin & Wound Care, 36 0 (9): 0 470--480, 2023

work page 2023