A review of artificial intelligence in wound care

Abstract

Our aging population, diabetes, and obesity have fueled the growth of chronic wounds seen throughout the world. Often, wounds are a marker of poor health that leads to increased mortality rates. However, the diagnosis and treatment of these wounds are challenging. Incorrectly differentiating between chronic wounds and other complex conditions can lead to adverse events. Artificial intelligence (AI) has been shown to offer some early benefits, and we hypothesized that it may enhance wound care but also carry some notable risks. We performed a detailed search using PubMed, Scopus, Cumulated Index in Nursing and Allied Health Literature, and Web of Science for AI applications in wound care. AI was found to be applied to wound diagnosis and characterization, wound monitoring for tissue change, daily therapy, and prevention and prognostics. AI made for more efficient and accurate wound assessments, less painful assessments of chronic wounds, more personalized treatment, and improved prognostic prediction capabilities. AI also allowed for more precise at-home observation and care, facilitating earlier wound treatment as needed. Challenges associated with AI included how to best allocate AI-assisted technologies equitably, how to safely maintain patient data, and how to diversify datasets for algorithm training. Because the algorithms are not transparent, validating findings may be challenging. AI presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care. More research needs to be done into how to best incorporate AI into daily clinical practice while keeping clinicians aware of the potential risks of using these evolving technologies.

Keywords

Artificial intelligence / wound healing / wound care / hard-to-heal wounds / chronic wounds / ulcers

References

[1] Carter MJ,Haught R,Cartwright D.Chronic wound prevalence and the associated cost of treatment in Medicare beneficiaries: changes between 2014 and 2019.J Med Econ2023;26:894-901

[2] Nussbaum SR,Fife CE.An economic evaluation of the impact, cost, and medicare policy implications of chronic nonhealing wounds.Value Health2018;21:27-32

[3] Khalid N,Bilal M,Qadir J.Privacy-preserving artificial intelligence in healthcare: techniques and applications.Comput Biol Med2023;158:106848

[4] Sharma M,Nair M,Svedberg P.Artificial intelligence applications in health care practice: scoping review.J Med Internet Res2022;24:e40238 PMCID:PMC9582911

[5] Guni A,Zhang J,Ashrafian H.Artificial intelligence in surgery: the future is now.Eur Surg Res2024;65:22-39

[6] Athanasopoulou K,Adamopoulos PG.Artificial intelligence: the milestone in modern biomedical research.BioMedInformatics2022;2:727-44

[7] Morris MX,Caneva T,Orgill DP.Current and future applications of artificial intelligence in surgery: implications for clinical practice and research.Front Surg2024;11:1393898 PMCID:PMC11111929

[8] Patel NP.Wound education: American medical students are inadequately trained in wound care.Ann Plast Surg2007;59:53-5; discussion 55

[9] Sürme Y,Çürük GN.Knowledge and practices of nurses regarding wound healing.J Perianesth Nurs2018;33:471-8

[10] Weenig RH,Dahl PR.Skin ulcers misdiagnosed as pyoderma gangrenosum.N Engl J Med2002;347:1412-8

[11] Birkner M,von den Driesch P.Computer-assisted differential diagnosis of pyoderma gangrenosum and venous ulcers with deep neural networks.J Clin Med2022;11:7103 PMCID:PMC9740900

[12] Hüsers J,Richter ML.An image based object recognition system for wound detection and classification of diabetic foot and venous leg ulcers.Stud Health Technol Inform2022;294:63-7

[13] Swerdlow M,Yaakov R.Simultaneous segmentation and classification of pressure injury image data using Mask-R-CNN.Comput Math Methods Med2023;2023:3858997 PMCID:PMC9911250

[14] Zahia S,Garcia-Zapirain B.Tissue classification and segmentation of pressure injuries using convolutional neural networks.Comput Methods Programs Biomed2018;159:51-8

[15] Chang CW,Chang DH et al.Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis.PLoS One2022;17:e0264139 PMCID:PMC8853507

[16] Eldem H,Işıklı OY.Alexnet architecture variations with transfer learning for classification of wound images.Eng Sci Technol Int J2023;45:101490

[17] Mohammed HT,Babb D,Mannion D.A time motion study of manual versus artificial intelligence methods for wound assessment.PLoS One2022;17:e0271742 PMCID:PMC9333325

[18] Lau CH,Yip TF.An artificial intelligence-enabled smartphone app for real-time pressure injury assessment.Front Med Technol2022;4:905074 PMCID:PMC9541137

[19] Chan KS,Tan AHM.Clinical validation of an artificial intelligence-enabled wound imaging mobile application in diabetic foot ulcers.Int Wound J2022;19:114-24 PMCID:PMC8684856

[20] Aldoulah ZA,Molyet R.A novel fused multi-class deep learning approach for chronic wounds classification.Appl Sci2023;13:11630

[21] Veredas F,Morente L.Binary tissue classification on wound images with neural networks and bayesian classifiers.IEEE Trans Med Imaging2010;29:410-27

[22] Lien AS,Wei J,Yeh J.A granulation tissue detection model to track chronic wound healing in DM foot ulcers.Electronics2022;11:2617

[23] Liu Z,Agu E.Diabetic foot ulcer ischemia and infection classification using efficientnet deep learning models.IEEE Open J Eng Med Biol2022;3:189-201 PMCID:PMC9842228

[24] Viswanathan V,Selvaraj B,Kumar R.A clinical study to evaluate autofluorescence imaging of diabetic foot ulcers using a novel artificial intelligence enabled noninvasive device.Int J Low Extrem Wounds2024;23:169-76

[25] Chairat S,Dissaneewate T,Kongpanichakul L.AI-assisted assessment of wound tissue with automatic color and measurement calibration on images taken with a smartphone.Healthcare2023;11:273 PMCID:PMC9858639

[26] Kavitha I,Ramakrishnan S.Analysis of chronic wound images using factorization based segmentation and machine learning methods. In: Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics (ICCBB '17); New York, USA. pp. 74-8.

[27] Kaile K,Mahadevan J.Low-cost smartphone based imaging device to detect subsurface tissue oxygenation of wounds. In: Optics and Biophotonics in Low-Resource Settings V; San Francisco, USA. 2019. pp. 62-5.

[28] Zoppo G,Pittarello M.AI technology for remote clinical assessment and monitoring.J Wound Care2020;29:692-706

[29] Guadagnin R,Santana LA.An image mining based approach to detect pressure ulcer stage.Pattern Recognit Image Anal2014;24:292-6

[30] Mondragon N. Pressure injury. In: StatPearls [Internet]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK557868/. [Last accessed on 2 Nov 2024]

[31] Al Aboud AM, Manna B. Wound pressure injury management. In: StatPearls [Internet]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK532897/. [Last accessed on 2 Nov 2024]

[32] Gabison S,Evans G,Dutta T.Measuring repositioning in home care for pressure injury prevention and management.Sensors2022;22:7013 PMCID:PMC9504953

[33] Danilovich I,Reimche A,Mikhaylovskiy N.Video monitoring over anti-decubitus protocol execution with a deep neural network to prevent pressure ulcer.Annu Int Conf IEEE Eng Med Biol Soc2021;2021:1384-7

[34] Ni TF,Chen CK,Wang J.Can a prolonged healing pressure injury be benefited by using an AI mattress? A case study.BMC Geriatr2024;24:307 PMCID:PMC10986049

[35] Kalasin S,Surareungchai W.Intelligent wearable sensors interconnected with advanced wound dressing bandages for contactless chronic skin monitoring: artificial intelligence for predicting tissue regeneration.Anal Chem2022;94:6842-52

[36] Topaz M,Dowding D.Automated identification of wound information in clinical notes of patients with heart diseases: developing and validating a natural language processing application.Int J Nurs Stud2016;64:25-31

[37] Robnik-Sikonja M,Kononenko I.Comprehensible evaluation of prognostic factors and prediction of wound healing.Artif Intell Med2003;29:25-38

[38] Ngo QC,Kumar DK.Computerised prediction of healing for venous leg ulcers.Sci Rep2022;12:17962 PMCID:PMC9606374

[39] Chen CL,Hung LP.Applying AIoT image recognition for prognosis of wound healing in long-term care residential facility. Wireless Netw 2023.

[40] Alderden J,Wilson A.Predicting pressure injury in critical care patients: a machine-learning model.Am J Crit Care2018;27:461-8 PMCID:PMC6247790

[41] Cai JY,Song YP.Predicting the development of surgery-related pressure injury using a machine learning algorithm model.J Nurs Res2020;29:e135 PMCID:PMC7808354

[42] Lee SK,Ahn J,Jang DE.Identifying the risk factors associated with nursing home residents’ pressure ulcers using machine learning methods.Int J Environ Res Public Health2021;18:2954 PMCID:PMC8001016

[43] Lustig M,Bryant R.A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements.Int Wound J2022;19:1339-48 PMCID:PMC9493225

[44] Shi L,Zhang T.A potent weighted risk model for evaluating the occurrence and severity of diabetic foot ulcers.Diabetol Metab Syndr2021;13:92 PMCID:PMC8407043

[45] Nanda R,Patel S.Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity.Med Biol Eng Comput2022;60:2349-57

[46] Schäfer Z,Svendsen K,Thomsen TR.Toward machine-learning-based decision support in diabetes care: a risk stratification study on diabetic foot ulcer and amputation.Front Med2021;7:601602 PMCID:PMC7931152

[47] Schäfer Z,Svendsen K,Rolighed Thomsen T.Toward machine-learning-based decision support in diabetes care: a risk stratification study on diabetic foot ulcer and amputation.Front Med2020;7:601602 PMCID:PMC7931152

[48] Xie P,Deng B.An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer.Int Wound J2022;19:910-8 PMCID:PMC9013600

[49] Morris MX,Rajesh A,Phillips BT.Ethical, legal, and financial considerations of artificial intelligence in surgery.Am Surg2023;89:55-60

[50] World Health Organization. WHO outlines considerations for regulation of artificial intelligence for health. 2023. Available from: https://www.who.int/news/item/19-10-2023-who-outlines-considerations-for-regulation-of-artificial-intelligence-for-health. [Last accessed on 2 Nov 2024]

[51] Nutifafa Cudjoe A. Ethical implications of artificial intelligence in the healthcare sector. Available from: https://www.researchgate.net/profile/Nutifafa-Amedior/publication/371806117_Ethical_Implications_of_Artificial_Intelligence_in_the_Healthcare_Sector/links/66659941b769e769192559d4/Ethical-Implications-of-Artificial-Intelligence-in-the-Healthcare-Sector.pdf. [Last accessed on 2 Nov 2024]

[52] Sadiq A,Bayat A. Chapter 8 Genetics of keloid scarring. In: Textbook on Scar Management: State of the Art Management and Emerging Technologies [Internet]. Cham: Springer International Publishing; 2020. pp. 61-76. Available from: https://www.ncbi.nlm.nih.gov/books/NBK586075/. [Last accessed on 2 Nov 2024]

[53] Schmieder SJ. Hypertrophic scarring. In: StatPearls [Internet]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK470176/. [Last accessed on 2 Nov 2024]

[54] Kim J,Lee YN.Predicting the severity of postoperative scars using artificial intelligence based on images and clinical data.Sci Rep2023;13:13448 PMCID:PMC10439171

[55] Chang CP,Fann WC.Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical parameters based on machine learning algorithms.Heliyon2024;10:e29578 PMCID:PMC11066613

[56] Haas R. Artificial intelligence for the prediction of sepsis in adults. In: CADTH Horizon Scan. Available from: https://www.ncbi.nlm.nih.gov/books/NBK596676/. [Last accessed on 2 Nov 2024]

[57] Henry KE,Parent C.Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing.Nat Med2022;28:1447-54

[58] Le DTP.Unveiling the role of artificial intelligence for wound assessment and wound healing prediction.Explor Med2023;4:589-611