Page 72 - Read Online
P. 72

Page 375                                                      Ganesan et al. Art Int Surg 2024;4:364-75  https://dx.doi.org/10.20517/ais.2024.68

               34.      Ni TF, Wang JL, Chen CK, Shih F, Wang J. Can a prolonged healing pressure injury be benefited by using an AI mattress? A case
                   study. BMC Geriatr 2024;24:307.  DOI  PubMed  PMC
               35.      Kalasin S, Sangnuang P, Surareungchai W. Intelligent wearable sensors interconnected with advanced wound dressing bandages for
                   contactless chronic skin monitoring: artificial intelligence for predicting tissue regeneration. Anal Chem 2022;94:6842-52.  DOI
                   PubMed
               36.      Topaz M, Lai K, Dowding D, et al. Automated identification of wound information in clinical notes of patients with heart diseases:
                   developing and validating a natural language processing application. Int J Nurs Stud 2016;64:25-31.  DOI  PubMed
               37.      Robnik-Sikonja M, Cukjati D, Kononenko I. Comprehensible evaluation of prognostic factors and prediction of wound healing. Artif
                   Intell Med 2003;29:25-38.  DOI  PubMed
               38.      Ngo QC, Ogrin R, Kumar DK. Computerised prediction of healing for venous leg ulcers. Sci Rep 2022;12:17962.  DOI  PubMed
                   PMC
               39.      Chen CL, Chiang SC, Hung LP, Jhang SJ. Applying AIoT image recognition for prognosis of wound healing in long-term care
                   residential facility. Wireless Netw 2023.  DOI
               40.      Alderden J, Pepper GA, Wilson A, et al. Predicting pressure injury in critical care patients: a machine-learning model. Am J Crit Care
                   2018;27:461-8.  DOI  PubMed  PMC
               41.      Cai JY, Zha ML, Song YP, Chen HL. Predicting the development of surgery-related pressure injury using a machine learning
                   algorithm model. J Nurs Res 2020;29:e135.  DOI  PubMed  PMC
               42.      Lee SK, Shin JH, Ahn J, Lee JY, Jang DE. Identifying the risk factors associated with nursing home residents’ pressure ulcers using
                   machine learning methods. Int J Environ Res Public Health 2021;18:2954.  DOI  PubMed  PMC
               43.      Lustig M, Schwartz D, Bryant R, Gefen A. A machine learning algorithm for early detection of heel deep tissue injuries based on a
                   daily history of sub-epidermal moisture measurements. Int Wound J 2022;19:1339-48.  DOI  PubMed  PMC
               44.      Shi L, Wei H, Zhang T, et al. A potent weighted risk model for evaluating the occurrence and severity of diabetic foot ulcers. Diabetol
                   Metab Syndr 2021;13:92.  DOI  PubMed  PMC
               45.      Nanda R, Nath A, Patel S, Mohapatra E. Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity.
                   Med Biol Eng Comput 2022;60:2349-57.  DOI  PubMed
               46.      Schäfer Z, Mathisen A, Svendsen K, Engberg S, Thomsen TR, Kirketerp-Møller K. Toward machine-learning-based decision support
                   in diabetes care: a risk stratification study on diabetic foot ulcer and amputation. Front Med 2021;7:601602.  DOI  PubMed  PMC
               47.      Schäfer Z, Mathisen A, Svendsen K, Engberg S, Rolighed Thomsen T, Kirketerp-Møller K. Toward machine-learning-based decision
                   support in diabetes care: a risk stratification study on diabetic foot ulcer and amputation. Front Med 2020;7:601602.  DOI  PubMed
                   PMC
               48.      Xie P, Li Y, Deng B, et al. An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic
                   foot ulcer. Int Wound J 2022;19:910-8.  DOI  PubMed  PMC
               49.      Morris MX, Song EY, Rajesh A, Asaad M, Phillips BT. Ethical, legal, and financial considerations of artificial intelligence in surgery.
                   Am Surg 2023;89:55-60.  DOI  PubMed
               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, Khumalo NP, 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, Ferrer-Bruker 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, Oh I, Lee YN, et al. Predicting the severity of postoperative scars using artificial intelligence based on images and clinical data.
                   Sci Rep 2023;13:13448.  DOI  PubMed  PMC
               55.      Chang CP, Lin CJ, Fann WC, Hsieh CH. Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical
                   parameters based on machine learning algorithms. Heliyon 2024;10:e29578.  DOI  PubMed  PMC
               56.      Haas R, McGill SC. 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, Adams R, Parent C, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system
                   and its effects on sepsis treatment timing. Nat Med 2022;28:1447-54.  DOI  PubMed
               58.      Le DTP, Pham TD. Unveiling the role of artificial intelligence for wound assessment and wound healing prediction. Explor Med
                   2023;4:589-611.  DOI
   67   68   69   70   71   72   73   74   75   76   77