Mapping the use of artificial intelligence for skin injury assessment and care in hospitalized patients: A scoping review
-
Angelo Antonio Paulino Martins Zanetti
,
Denise Desconsi
,
Rafaella Manhoni Lima De Miranda
,
Luisa Brolacci Lana
,
Lucas Daniel Del Rosso Calache
,
Silvana Andréa Molina Lima
,
Clarita Terra Rodrigues Serafim
Abstract
Background & Aim: Skin injuries are frequent hospital complications, and the role of artificial intelligence in management remains unclear. This review aimed to identify, map, and analyze the evidence on the use of artificial intelligence in the assessment, monitoring, and management of skin injuries in hospitalized patients worldwide.
Methods & Materials: A scoping review was conducted following the Joanna Briggs Institute guidance and the PRISMA Extension for Scoping Reviews (PRISMA-ScR). Searches were carried out in Embase, PubMed, Scopus, CINAHL, Cochrane Library, Web of Science, SciELO, BVS, LILACS, and the CAPES thesis and dissertation catalog. Eligible sources included primary studies, technical notes, dissertations, and theses. All references were organized in EndNote Web and transferred to Rayyan to support duplicate removal and facilitate screening by reviewers.
Results: The search resulted in the identification of 1,240 studies, of which eight were included and published in English. Most studies are technological development studies with samples ranging from 10 to 5,729 images or participants. Studies have shown that artificial intelligence techniques applied to pressure injuries, including Convolutional Neural Networks, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, improve detection, measurement, classification, risk prediction, and clinical decision support, potentially reducing workload and enhancing care safety.
Conclusion: The application of artificial intelligence in the domain of skin injuries revealed a variety of uses. However, it was predominantly focused on the specific context of pressure injuries in hospitalized individuals. Consequently, a noticeable gap in the literature was identified regarding alternative categories of injuries affecting this population segment.
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7. Sardo PM, Teixeira JP, Machado AM, Oliveira BF, Alves IM. A systematic review of the prevalence and incidence of pressure ulcers/injuries in hospital emergency services. Journal of Tissue Viability. 2023 May 1;32(2):179-87. doi: 10.1016/j.jtv.2023.02.001.
8. Zhang H, Ma Y, Wang Q, Zhang X, Han L. Incidence and prevalence of pressure injuries in children patients: A systematic review and meta-analysis. Journal of Tissue Viability. 2022 Feb 1;31(1):142-51. doi: 10.1016/j.jtv.2021.07.003.
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23. Center for Open Science. Open Science Framework (OSF) [Internet]. Charlottesville (VA): Center for Open Science; [cited 2024 Nov 4]. Available from: https://osf.io
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26. Zahia S, Sierra-Sosa D, Garcia-Zapirain B, Elmaghraby A. Tissue classification and segmentation of pressure injuries using convolutional neural networks. Computer methods and programs in biomedicine. 2018 Jun 1;159:51-8. doi: 10.1016/j.cmpb.2018.02.018.
27. Chun X, Pan L, Lin Y, Ye L, Liang H, Tao J, Luo Y. A model for predicting 7‐day pressure injury outcomes in paediatric patients: A machine learning approach. Journal of Advanced Nursing. 2021 Mar;77(3):1304-14. doi: 10.1111/jan.14680.
28. Wang Y, Jiang X, Yu K, Shi F, Qin L, Zhou H, Cai F. Infrared thermal images classification for pressure injury prevention incorporating the convolutional neural networks. IEEE Access. 2021 Jan 12;9:15181-90. doi: 10.1109/ACCESS.2021.3051095.
29. Chang CW, Christian M, Chang DH, Lai F, Liu TJ, Chen YS, Chen WJ. Deep learning approach based on superpixel segmentation-assisted labeling for automatic pressure ulcer diagnosis. PloS One. 2022 Feb 17;17(2):e0264139. doi: 10.1371/journal.pone.0264139.
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32. Chen X, Tang S, Qin Y, Zhou S, Zhang L, Huang Y, Chen Z. A predictive model of pressure injury in children undergoing living donor liver transplantation based on a machine learning algorithm. Journal of Advanced Nursing. 2025 Jun;81(6):3003-12. doi: 10.1111/jan.16449
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2. Roger M, Fullard N, Costello L, Bradbury S, Markiewicz E, O'Reilly S, Darling N, Ritchie P, Määttä A, Karakesisoglou I, Nelson G. Bioengineering the microanatomy of human skin. Journal of Anatomy. 2019 Apr;234(4):438-55. doi: 10.1111/joa.12942.
3. Castiblanco-Montañez RA, Agudelo-Turriago AM, Salas-Pérez JY, Pérez-Pérez MM, Guzmán-Ruiz MY. [Characterization of skin lesions in a health institution in Bogotá]. [In Spanish]. Revista Ciencia y Cuidado. 2022 May 1;19(2):50-60. doi: 10.22463/17949831.3213. doi: 10.22463/17949831.3213.
4. Monteiro DS, Borges EL, Spira JA, Garcia TD, Matos SS. Incidence of skin injuries, risk, and clinical characteristics of critical patients. Texto & Contexto- Enfermagem. 2021 Aug 2;30:e20200125. doi: 10.1590/1980-265x-tce-2020-0125.
5. National Pressure Injury Advisory Panel. NPIAP pressure injury stages [Internet]. Chicago: NPIAP; 2016 [cited 2024 Nov 4]. Available from: https://cdn.ymaws.com/npiap.com/resource/resmgr/online_store/npiap_pressure_injury_stages.pdf
6. Jesus MAP, Pires PS, Biondo CS, Matos RM. Incidence of pressure injury in hospitalized patients and associated risk factors [In Portuguese]. Reben. 2020;34:e36587.
7. Sardo PM, Teixeira JP, Machado AM, Oliveira BF, Alves IM. A systematic review of the prevalence and incidence of pressure ulcers/injuries in hospital emergency services. Journal of Tissue Viability. 2023 May 1;32(2):179-87. doi: 10.1016/j.jtv.2023.02.001.
8. Zhang H, Ma Y, Wang Q, Zhang X, Han L. Incidence and prevalence of pressure injuries in children patients: A systematic review and meta-analysis. Journal of Tissue Viability. 2022 Feb 1;31(1):142-51. doi: 10.1016/j.jtv.2021.07.003.
9. Jackson D, Sarki AM, Betteridge R, Brooke J. Medical device-related pressure ulcers: a systematic review and meta-analysis. International Journal of Nursing Studies. 2019 Apr 1;92:109-20. doi: 10.1016/j.ijnurstu.2019.02.006.
10. Beeckman D, Van Tiggelen H. International Skin Tear Advisory Panel (ISTAP) classification system: Portuguese version [Internet]. Ghent: SKINT; 2018 [cited 2024 Nov 4]. Available from: https://www.skintghent.be
11. Yang S, Liang X, She J, Tian J, Wen Z, Tao Y, Wang H, Zhang X. Prevalence and incidence of skin tear in older adults: A systematic review and meta-analysis. Journal of Tissue Viability. 2024 Nov 1;33(4):1017-24. doi: 10.1016/j.jtv.2024.06.010.
12. Gray M, Bliss DZ, Doughty DB, Ermer-Seltun J, Kennedy-Evans KL, Palmer MH. Incontinence-associated dermatitis: A consensus. Journal of Wound Ostomy & Continence Nursing. 2007 Jan 1;34(1):45-54. doi: 10.1097/00152192-200701000-00008.
13. Zulkowski K. Diagnosing and treating moisture-associated skin damage. Advances in Skin & Wound Care. 2012 May 1;25(5):231-6. doi: 10.1097/01. ASW.0000414707.33267.92.
14. Wei M, Yang D, Wu L, Chen W, Chen Y, Fu Q. The prevalence of incontinence-associated dermatitis in hospitalized patients in China: A systematic review and meta-analysis. Advances in Skin & Wound Care. 2020 Oct 1;33(10):1-7. doi: 10.1097/01.ASW.0000695764.47424.aa.
15. Balaguer-López E, Estañ-Capell J, Dolz MCR, Ventura MCB, López MR, García-Molina P. Incidence of incontinence-associated dermatitis in hospitalized neonates: adaptation and validation of a severity scale. Anales de Pediatría. 2024;100(6):420-7. doi: 10.1016/j.anpede.2024.04.015.
16. Cazzato G, Rongioletti F. Artificial intelligence in dermatopathology: Updates, strengths, and challenges. Clinics in Dermatology. 2024 Sep 1;42(5):437-42. doi: 10.1016/j.clindermatol.2024.06.010.
17. Souza Filho EM, Fernandes FA, Soares CLA, Seixas FL, Santos AASMD, Gismondi RA, et al. Artificial intelligence in cardiology: Concepts, tools, and challenges—"The horse runs, you need to be the jockey" [In Portuguese]. Arq Bras Cardiol. 2020;114(4):718-25. doi: 10.36660/abc.20180431.
18. Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology. 2019 Mar 26;73(11):1317-35. doi: 10.1016/j.jacc.2018.12.054.
19. Peters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, McInerney P, Godfrey CM, Khalil H. Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis. 2020 Oct 1;18(10):2119-26. doi: 10.11124/JBIES-20-00167.
20. Pollock D, Davies EL, Peters MD, Tricco AC, Alexander L, McInerney P, Godfrey CM, Khalil H, Munn Z. Undertaking a scoping review: A practical guide for nursing and midwifery students, clinicians, researchers, and academics. Journal of Advanced Nursing. 2021 Apr;77(4):2102-13. doi: 10.1111/jan.14743.
21. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MD, Horsley T, Weeks L, Hempel S. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine. 2018 Oct 2;169(7):467-73. doi: 10.7326/M18-0850.
22. Zanetti AAPM, Desconsi D, Lana LB, Calache LDDR, Lima SAM, Clarita TRS. Artificial Intelligence use in the skin injury context: A scoping review protocol. Rev. enferm. UFPE online. 2025, 19(2) e263215. doi: 10.5205/1981-8963.2025.263215
23. Center for Open Science. Open Science Framework (OSF) [Internet]. Charlottesville (VA): Center for Open Science; [cited 2024 Nov 4]. Available from: https://osf.io
24. Markova A, Mostow EN. US skin disease assessment: ulcer and wound care. Dermatologic Clinics. 2012 Jan 1;30(1):107-11.doi: 10.1016/j.det.2011.08.005.
25. Li D, Mathews C. Automated measurement of pressure injury through image processing. Journal of Clinical Nursing. 2017 Nov;26(21-22):3564-75. doi: 10.1111/jocn.13726.
26. Zahia S, Sierra-Sosa D, Garcia-Zapirain B, Elmaghraby A. Tissue classification and segmentation of pressure injuries using convolutional neural networks. Computer methods and programs in biomedicine. 2018 Jun 1;159:51-8. doi: 10.1016/j.cmpb.2018.02.018.
27. Chun X, Pan L, Lin Y, Ye L, Liang H, Tao J, Luo Y. A model for predicting 7‐day pressure injury outcomes in paediatric patients: A machine learning approach. Journal of Advanced Nursing. 2021 Mar;77(3):1304-14. doi: 10.1111/jan.14680.
28. Wang Y, Jiang X, Yu K, Shi F, Qin L, Zhou H, Cai F. Infrared thermal images classification for pressure injury prevention incorporating the convolutional neural networks. IEEE Access. 2021 Jan 12;9:15181-90. doi: 10.1109/ACCESS.2021.3051095.
29. Chang CW, Christian M, Chang DH, Lai F, Liu TJ, Chen YS, Chen WJ. Deep learning approach based on superpixel segmentation-assisted labeling for automatic pressure ulcer diagnosis. PloS One. 2022 Feb 17;17(2):e0264139. doi: 10.1371/journal.pone.0264139.
30. Pandey B, Joshi D, Arora AS, Upadhyay N, Chhabra HS. A deep learning approach for automated detection and segmentation of pressure ulcers using infrared-based thermal imaging. IEEE Sensors Journal. 2022 Jun 27;22(15):14762-8. doi: 10.1109/JSEN.2022.3184105.
31. Lee LL, Chen SL. The application of hyperspectral imaging to the measurement of pressure injury area. International Journal of Environmental Research and Public Health. 2023 Feb 6;20(4):2851. doi: 10.3390/ijerph20042851.
32. Chen X, Tang S, Qin Y, Zhou S, Zhang L, Huang Y, Chen Z. A predictive model of pressure injury in children undergoing living donor liver transplantation based on a machine learning algorithm. Journal of Advanced Nursing. 2025 Jun;81(6):3003-12. doi: 10.1111/jan.16449
33. Kourounis G, Elmahmudi AA, Thomson B, Hunter J, Ugail H, Wilson C. Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals. Postgraduate medical Journal. 2023 Dec;99(1178):1287-94.doi: 10.1093/postmj/qgad095.
34. Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, Alseidi A, Redan JA, Alfieri S, Costamagna G, Boškoski I. Computer vision in surgery: From potential to clinical value. NPJ Digital Medicine. 2022 Oct 28;5(1):163. doi: 10.1038/s41746-022-00707-5.
35. Yousef R, Khan S, Gupta G, Siddiqui T, Albahlal BM, Alajlan SA, Haq MA. U-Net-based models towards optimal MR brain image segmentation. Diagnostics. 2023 May 4;13(9):1624. doi: 10.3390/diagnostics13091624.
36. Wei X, Chen X, Lai C, Zhu Y, Yang H, Du Y. Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region‐Based CNN Architectures. BioMed Research International. 2021;2021(1):9956983. doi: 10.1155/2021/9956983.
37. Tang Y, Tan D, Li H, Zhu M, Li X, Wang X, Wang J, Wang Z, Gao C, Wang J, Han A. RTC_TongueNet: An improved tongue image segmentation model based on DeepLabV3. Digital Health. 2024 Mar;10:20552076241242773. doi: 10.1177/20552076241242773.
38. Wang J, Liu X. Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network. Computer Methods and Programs in Biomedicine. 2021 Aug 1;207:106210. doi: 10.1016/j.cmpb.2021.106210.
39. Lyu T, Yang G, Zhao X, Shu H, Luo L, Chen D, Xiong J, Yang J, Li S, Coatrieux JL, Chen Y. Dissected aorta segmentation using convolutional neural networks. Computer Methods and Programs in Biomedicine. 2021 Nov 1;211:106417. doi: 10.1016/j.cmpb.2021.106417.
40. Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017 Jul 21-26; Honolulu, HI. Los Alamitos (CA): IEEE Computer Society; 2017.
41. Hasanah U, Avian C, Darmawan JT, Bachroin N, Faisal M, Prakosa SW, Leu JS, Tsai CT. CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification. The International Journal of Cardiovascular Imaging. 2024 Apr;40(4):709-22. doi: 10.1007/s10554-023-03039-x.
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| Issue | Articles in Press | |
| Section | Review Article(s) | |
| Keywords | ||
| skin wounds and injuries artificial intelligence machine learning digital health | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Paulino Martins Zanetti A, Desconsi D, Manhoni Lima De Miranda R, Brolacci Lana L, Del Rosso Calache L, Molina Lima S, Terra Rodrigues Serafim C. Mapping the use of artificial intelligence for skin injury assessment and care in hospitalized patients: A scoping review. Nurs. pract. today. 2025;:X-X.
