Aplicações da inteligência artificial no cuidado com o pé diabético: revisão de escopo

Palavras-chave: Inteligência artificial, Diabetes mellitus, Pé diabético, Enfermagem, Cuidados de enfermagem

Resumo

Objetivo: mapear e sintetizar a literatura científica sobre a aplicação da inteligência artificial na saúde para o cuidado/manejo do pé diabético e identificar lacunas de evidências para a prática de enfermagem. Método: revisão de escopo conduzida conforme as diretrizes do Instituto Joanna Briggs. As buscas foram realizadas em seis bases de dados internacionais e na literatura cinzenta, sem restrição de idioma ou data. Resultados: foram incluídos 101 estudos. A inteligência artificial tem sido incorporada à aplicativos móveis e sistemas de telemonitoramento para análise de imagens, possibilitando apoio diagnóstico e suporte à decisão clínica. Apesar dos avanços, aspectos éticos, de segurança da informação e riscos de vieses algorítmicos são pouco explorados. A integração ao cuidado de enfermagem mostrou-se incipiente, sem avaliação dos impactos na assistência em termos relacionais e operacionais. Conclusões: a inteligência artificial mostra-se promissora, porém requer diretrizes éticas, infraestrutura adequada e o protagonismo da enfermagem no desenvolvimento e implementação.

Downloads

Não há dados estatísticos.

Biografia do Autor

Raissa Vitória Kamada, Universidade Federal de Alfenas (UNIFAL)
Suzana Rangel Magalhães, Universidade Federal de Alfenas (UNIFAL)
Yasmim Ribeiro Fracaroli, Universidade Federal de Alfenas (UNIFAL)
Silvana Maria Coelho Leite Fava, Universidade Federal de Alfenas (UNIFAL)
Eliza Maria de Rezende Dázio, Universidade Federal de Alfenas (UNIFAL)
Alice Silva Costa, Universidade Federal de Alfenas (UNIFAL)
Isabelle Cristinne Pinto Costa, Universidade Federal de Alfenas (UNIFAL)
Lilian Cristiane Gomes, Universidade Federal de Alfenas (UNIFAL)

Referências

Bus SA, Lavery LA, Monteiro-Soares M, Rasmussen A, Raspovic A, Sacco ICN, et al. Guidelines on the prevention of foot ulcers in persons with diabetes (IWGDF 2019 update). Diabetes Metab Res Rev. 2020;36(suppl 1):e3269. DOI: https://doi.org/10.1002/dmrr.3651

Zhang H, Huang C, Bai J, Wang J. Effect of diabetic foot ulcers and other risk factors on the prevalence of lower extremity amputation: a meta-analysis. Int Wound J. 2023;20(8):3035–47. DOI: https://doi.org/10.1111/iwj.141793

Sacco ICN, Lucovéis MLS, Thuler SR, Parisi MCR. Diagnóstico e prevenção de úlceras no pé diabético. São Paulo: Diretriz Oficial da Sociedade Brasileira de Diabetes; 2022. DOI: https://doi.org/10.29327/5412848.2024-11

Duarte Júnior EG, Lopes CF, Gaio DRF, Mariúba JVO, Cerqueira LO, Manhanelli Filho MAB, et al. Diretrizes da Sociedade Brasileira de Angiologia e de Cirurgia Vascular sobre o pé diabético 2023. J Vasc Bras. 2024; 23:e20230087. DOI: https://doi.org/10.1590/1677-5449.202300871

Torres DR, Wermelinger ED, Ferreira AP. Aplicação da Inteligência Artificial na Atenção Primária à Saúde: revisão de escopo e avaliação crítica. Saúde debate. 2025;49(145): e10070. DOI: https://doi.org/10.1590/2358-2898202514510070P

Raposo DRA, Alves-Júnior JS, Silva DTO, Alves RA, Santos DCM, Alves TM, et al. Avanços na assistência de enfermagem: uma revisão de escopo sobre o uso da Inteligência Artificial na ciência do cuidar. Contrib. cienc. soc. 2024;17(7):e8854. DOI: https://doi.org/10.55905/revconv.17n.7-417

Gomes AM, Dantas LB, Sobreira MMS, Araújo JNM, Tinôco JDS. Artificial intelligence in supporting the implementation of the nursing process: A scoping review. Rev. gaúch. enferm. 2025;45:e20250077. Disponível em: Disponível em: https://seer.ufrgs.br/index.php/rgenf/article/view/151062

O’Connor S, Yan Y, Thilo FJS, Felzmann H, Dowding D, Lee JJ. Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs. 2022;00:1–18. DOI: https://doi.org/10.1111/jocn.16478

Ardelean A, Balta DF, Neamtu C, Neamtu AA, Rosu M, Totolici B. Personalized and predictive strategies for diabetic foot ulcer prevention and management. Med Pharm Rep. 2024;97(4): 419-28. DOI: https://doi.org/10.15386/mpr-2818

Papazafiropoulou AK. Diabetes management in the era of artificial intelligence. Arch Med Sci Aterosclerose Dis. 2024;9:e122–e128. DOI: https://doi.org/10.5114/amsad/183420

Aromataris E, Lockwood C, Porritt K, Pilla B, Jordan Z, editors. JBI Manual for Evidence Synthesis. Adelaide: JBI;2024. Available from: https://synthesismanual.jbi.global

Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73. DOI: https://doi.org/10.7326/m18-0850

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. DOI: https://doi.org/10.1186/s13643-016-0384-4

Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Implement. 2021; 19(1):3–10. DOI: https://doi.org/10.11124/jbies-20-00167

Pollock D, Peters MDJ, Khalil H, McInerney P, Alexander L, Tricco AC, et al. Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evid Synth. 2023;21(3):520–32. DOI: https://doi.org/10.11124/jbies-22-00123

Hill A. Is artificial intelligence the key to better foot self-care in diabetes? The Diabetic Foot J. 2023;26(2):24–8. Available from: https://diabetesonthenet.com/wp-content/uploads/DFJ-26-2_24-28_Hill.pdf

Rippon MG, Fleming L, Chen T, Rogers AA, Ousey K. Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review. J Wound Care. 2024;33(4):229-42. DOI: https://doi.org/10.12968/jowc.2024.33.4.229

Apolinário PP, Zanchetta FC, Pittella CQP, Lima MHM. Avanços da tecnologia de inteligência artificial no cuidado de úlceras no pé diabético. Rev. Enferm. Atual In Derme. 2024;98(3):e024377. DOI: https://doi.org/10.31011/reaid-2024-v.98-n.3-art.2326

Mayya V, Tummala V, Reddy CU, Mishra P, Boddu R, Olivia D, et al. Applications of machine learning in diabetic foot ulcer diagnosis using multimodal images: a review. IJAM. 2023;53(3):1-10. DOI: https://www.iaeng.org/IJAM/issues_v53/issue_3/IJAM_53_3_10.pdf

Almufadi NF, Alhasson HF, Alharbi SS. E-DFu-Net: an efficient deep convolutional neural network models for diabetic foot ulcer classification. Biomol Biomed. 2025;25(2):445-60. DOI: https://doi.org/10.17305/bb.2024.11117

Sumithra MG, Venkatesan C. SwinDFU-Net: Deep learning transformer network for infection identification in diabetic foot ulcer. Technol. health care. 2025; 33(1):601-18. DOI: https://doi.org/10.3233/THC-241444

Wu L, Huang R, He X, Tang L, Ma X. Advances in machine learning-aided thermal imaging for early detection of diabetic foot ulcers: a review. Biosensors. 2024;14(12):614. DOI: https://doi.org/10.3390/bios14120614

Wang C, Yu Z, Long Z, Zhao H, Wang Z. A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning. Sci Rep. 2024;14:29877. DOI: https://doi.org/10.1038/s41598-024-80691-w

Sidhu AS, Harbuzova V. Emerging technologies for the management of diabetic foot ulceration: a review. Front Clin Diabetes Healthc. 2024;12(5):1440209. DOI: https://doi.org/10.3389/fcdhc.2024.1440209

Almufadi N, Alhasson HF. Classification of diabetic foot ulcers from images using machine learning approach. Diagnostics (Basel). 2024;14(16):1807. DOI: https://doi.org/10.3390/diagnostics14161807

Biswas S, Mostafiz R, Uddin MS, Paul BK. XAI-FusionNet: diabetic foot ulcer detection based on multiscale feature fusion with explainable artificial intelligence. Heliyon. 2024;10:e31228. DOI: https://doi.org/10.1016/j.heliyon.2024.e31228

Wang Z, Tan X, Xue Y, Xiao C, Yue K, Lin K, et al. Smart diabetic foot ulcer scoring system. Sci Rep. 2024;14:11588. DOI: https://doi.org/10.1038/s41598-024-62076-1

Fadhel MA, Alzubaidi L, Gu Y, Santamaría J, Duan Y. Real-time diabetic foot ulcer classification based on deep learning & parallel hardware computational tools. Multimed Tools Appl. 2024;83:70369–94. DOI: https://doi.org/10.1007/s11042-024-18304-x

Oei CW, Chan YM, Zhang X, Leo KH, Yong E, Chong RC, et al. Risk prediction of diabetic foot amputation using machine learning and explainable artificial intelligence. J Diabetes Sci Technol. 2024;19(4):1008-22. DOI: https://doi.org/10.1177/19322968241228606

Demirkol D, Erol ÇS, Tannier X, Özcan T, Aktaş Ş. Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center. Int Wound J. 2024;21(1):e14556. DOI: https://doi.org/10.1111/iwj.14556

Wu C, Xu C, Ou S, Wu X, Guo J, Qi Y, et al. A novel approach for diabetic foot diagnosis: Deep learning-based detection of lower extremity arterial stenosis. Diabetes Res Clin Pract. 2024;207:111032. DOI: https://doi.org/10.1016/j.diabres.2023.111032

Sun Y, Ge L, Ang YG, Lo ZJ, Liew H, Tan DM, et al. Cost-effectiveness and clinical outcomes of artificial intelligence-enhanced screening for diabetic foot ulcers: A simulation study. Ann Acad Med Singap. 2024;53(10):638-640. DOI: https://doi.org/10.47102/annals-acadmedsg.2024220

Dey AK, Kumari N, Motghare VM, Bapuji AT, Sai J, Thakur AK, et al. IDF23-0385 Technology advancement in the care of diabetic foot ulcers - the future perspective. Diabetes Res Clin Pract. 2024;209(1):111328. DOI: https://doi.org/10.1016/j.diabres.2024.111328

Sharma N, Mirza S, Rastogi A, Singh S, Mahapatra PK. Region-wise severity analysis of diabetic plantar foot thermograms. Biomed Tech (Berl). 2023;68(6):607-15. DOI: https://doi.org/10.1515/bmt-2022-0376

Cassidy B, Hoon YM, Pappachan JM, Ahmad N, Haycocks S, O'Shea C, et al. Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation. Diabetes Res Clin Pract. 2023;205:110951. DOI: https://doi.org/10.1016/j.diabres.2023.110951

Cao Z, Zeng Z, Xie J, Zhai H, Yin Y, Ma Y, et al. Diabetic plantar foot segmentation in active thermography using a two-stage adaptive gamma transform and a deep neural network. Sensors (Basel). 2023;23(20):8511. DOI: https://doi.org/10.3390/s23208511

Alqahtani A, Alsubai S, Rahamathulla MP, Gumaei A, Sha M, Zhang YD, et al. Empowering foot health: harnessing the adaptive weighted sub-gradient convolutional neural network for diabetic foot ulcer classification. Diagnostics (Basel). 2023;13(17):2831. DOI: https://doi.org/10.3390/diagnostics13172831

Sharma N, Mirza S, Rastogi A, Mahapatra PK. Utilizing mask R-CNN for automated evaluation of diabetic foot ulcer healing trajectories: a novel approach. IIETA. 2023;40(4):1601-10. DOI: https://doi.org/10.18280/ts.400428

Mousa KM, Mousa FA, Mohamed HS, Elsawy MM. Prediction of foot ulcers using artificial intelligence for diabetic patients at Cairo University Hospital, Egypt. SAGE Open Nurs. 2023;9:23779608231185873. DOI: https://doi.org/10.1177/23779608231185873

Gosak L, Svensek A, Lorber M, Stiglic G. Artificial intelligence based prediction of diabetic foot risk in patients with diabetes: a literature review. Appl. Sci. 2023;13(5):2823. DOI: https://doi.org/10.3390/app13052823

Poradzka AA, Czupryniak L. The use of the artificial neural network for three-month prognosis in diabetic foot syndrome. J Diabetes Complications. 2023;37(2):108392. DOI: https://doi.org/10.1016/j.jdiacomp.2022.108392

Pappachan JM, Cassidy B, Fernandez CJ, Chandrabalan V, Yap MH. The role of artificial intelligence technology in the care of diabetic foot ulcers: the past, the present, and the future. World J Diabetes. 2022;13(12):1131-9. DOI: https://doi.org/10.4239/wjd.v13.i12.1131

Huang HN, Zhang T, Yang CT, Sheen YJ, Chen HM, et al. Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments. Front Public Health. 2022; 10:969846. DOI: https://doi.org/10.3389/fpubh.2022.969846

Chan KS, Chan YM, Tan AHM, Liang S, Cho YT, Hong Q, et al. Clinical validation of an artificial intelligence-enabled wound imaging mobile application in diabetic foot ulcers. Int Wound J. 2022;19(1):114-24. DOI: https://doi.org/10.1111/iwj.13603

Munadi K, Saddami K, Oktiana M, Roslidar R, Muchtar K, Melinda M, et al. A deep learning method for early detection of diabetic foot using decision fusion and thermal images. Appl. Sci. 2022;12(15):7524. DOI: https://doi.org/10.3390/app12157524

Ahsan M, Naz S, Ahmad R, Ehsan H, Sikandar A. A deep learning approach for diabetic foot ulcer classification and recognition. Information. 2023;14(1):36. DOI: https://doi.org/10.3390/info14010036

Cassidy B, Reeves ND, Pappachan JM, Gillespie D, O'Shea C, Rajbhandari S, et al. The DFUC 2020 Dataset: analysis towards diabetic foot ulcer detection. touchREV Endocrinol. 2021;17(1):5-11. DOI: https://doi.org/10.17925/EE.2021.17.1.5

Filipe V, Teixeira P, Teixeira AA clustering approach for prediction of diabetic foot using thermal images. ICCSA. 2020; 12251:620-31. DOI: https://doi.org/ 10.1007/978-3-030-58808-3_45

Cruz-Vega I, Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno JJ, Ramirez-Cortes JM. Deep Learning classification for diabetic foot thermograms. Sensors (Basel). 2020;20(6):1762. DOI: https://doi.org/10.3390/s20061762

Guan H, Wang Y, Niu P, Zhang Y, Zhang Y, Miao R, et al. The role of machine learning in advancing diabetic foot: a review. Front Endocrinol (Lausanne). 2024;15:1245658. DOI: https://doi.org/10.3389/fendo.2024.1325434

Goyal M, Reeves ND, Davison AK, Rajbhandari S, Spragg J, Yap MH. Dfunet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans. Emerg. Top. Comput. Intell. 2020;4(5):728-39. DOI: https://doi.org/10.1109/TETCI.2018.2866254

Santos E, Santos F, Dallyson J, Aires K, Tavares JMRS, Veras R. Diabetic foot ulcers classification using a fine-tuned CNNs ensemble. IEEE Xplore. 2022;35:282-87, DOI: https://doi.org/10.1109/CBMS55023.2022.00056

Galdran A, Carneiro G, Ballester MAG. Convolutional Nets versus Vision Transformers for diabetic foot ulcer classification. DFUC. 2021;13183:21-9. DOI: https://doi.org/10.1007/978-3-030-94907-5_2

Amin J, Sharif M, Anjum MA, Khan HU, Malik MSA, Kadry S. An integrated design for classification and localization of diabetic foot ulcer based on CNN and YOLOV2-DFU models. IEEE Access. 2020;8:228586-97. DOI: https://doi.org/10.1109/ACCESS.2020.3045732

Reyes-Luévano J, Guerrero-Viramontes JA, Romo-AndradeJR, Funes-Gallanzi M. DFU_VIRnet: a novel visible-infrared CNN to improve diabetic foot ulcer classification and early detection of ulcer risk zones. Biomed Signal Process Control. 2023; 86:105341. DOI: https://doi.org/10.1016/j.bspc.2023.105341

Biswas S, Mostafiz R, Paul BK, Uddin KMM, Hadi A, Khamon F. DFU_XAI: A deep learning-based approach to diabetic foot ulcer detection using feature explainability. Biomedical Materials & Devices. 2024;2:1225–45. DOI: https://doi.org/10.1007/s44174-024-00165-5

Tulloch J, Zamani R, Akrami M. Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: a systematic review. IEEE Access. 2020;8:198977-199000. DOI: https://doi.org/10.1109/ACCESS.2020.3035327

Chemello G, Salvatori B, Morettini M, Tura A. Artificial intelligence methodologies applied to technologies for screening, diagnosis and care of the diabetic foot: a narrative review. Biosensors. 2022;12(11):985. DOI: https://doi.org/10.3390/bios121109857

Tehsin S, Kausar S, Jameel A. Diabetic wounds and artificial intelligence: A mini-review. World J Clin Cases. 2023;11(1):84-91. DOI: https://doi.org/10.12998/wjcc.v11.i1.84

Howard T, Ahluwalia R, Papanas N. The advent of artificial intelligence in diabetic foot medicine: a new horizon, a new order, or a false dawn? Int J Low Extrem Wounds. 2023;22(4):635-40. DOI: https://doi.org/10.1177/15347346211041866

Das SK, Roy P, Singh P, Diwakar M, Singh V, Maurya A, et al. Diabetic foot ulcer identification: A Review. Diagnostics (Basel). 2023;13(12):1998. DOI: https://doi.org/10.3390/diagnostics13121998

Dębiec-Bąk A, Skrzek A, Ptak A, Majerski K, Uiberlayová I, Stefańska M, et al. Advantages of thermovision imaging for PPPM approach to diabetic foot. APPPM. 2023;17:233-42. DOI: https://doi.org/10.1007/978-3-031-34884-6_13

Toofanee MAS, Dowlut S, Hamroun M, Tamine K, Petit V, Duong AK. DFU-SIAM a novel diabetic foot ulcer classification with deep learning. IEEE Access. 2023;11:98315-32. DOI: https://doi.org/10.1109/ACCESS.2023.3312531

Srass H, Ead JK, Armstrong DG. Adherence and the diabetic foot: high tech meets high touch? Sensors (Basel). 2023;23(15):6898. DOI: https://doi.org/10.3390/s23156898

Prakash RV, Kumar KS. Development of automatic segmentation techniques using convolutional neural networks to differentiate diabetic foot ulcers. IJACSA. 2022;13(11):521-26. DOI: https://doi.org/10.14569/IJACSA.2022.0131160

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(8):2349-57. DOI: https://doi.org/10.1007/s11517-022-02617-w

Troitskaya NI, Shapovalov KG, Mudrov VA. Possibilities of multilayer perceptron in complexing risk factors of diabetic foot syndrome. Bull Exp Biol Med. 2022;173(4):415-18. DOI: https://doi.org/10.1007/s10517-022-05559-3

Bouallal D, Douzi H, Harba R. Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet). J Med Eng Technol. 2022;46(5):378-92. DOI: https://doi.org/10.1080/03091902.2022.2077997

Khandakar A, Chowdhury MEH, Reaz MBI, Ali SHM, Kiranyaz S, Rahman T, et al. A novel machine learning approach for severity classification of diabetic foot complications using thermogram images. Sensors (Basel). 2022;22(11):4249. DOI: https://doi.org/10.3390/s22114249

Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, et al. Machine learning-based diabetic neuropathy and previous foot ulceration patients detection using electromyography and ground reaction forces during gait. Sensors (Basel). 2022;22(9):3507. DOI: https://doi.org/10.3390/s22093507

Khandakar A, Chowdhury MEH, Reaz MBI, Ali SHM, Abbas TO, Alam T, et al. Thermal change index-based diabetic foot thermogram image classification using machine learning techniques. Sensors (Basel). 2022;22(5):1793. DOI: https://doi.org/10.3390/s22051793

Costa T, Coelho L, Silva MF. Automatic segmentation of monofilament testing sites in plantar images for diabetic foot management. Bioengineering (Basel). 2022;9(3):86. DOI: https://doi.org/10.3390/bioengineering9030086

Wang S, Wang J, Zhu MX, Tan Q. Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers. PLoS One. 2022;17(12):e0278445. DOI: https://doi.org/10.1371/journal.pone.0278445

Muralidhara S, Lucieri A, Dengel A, Ahmed S. Holistic multi-class classification & grading of diabetic foot ulcerations from plantar thermal images using deep learning. Health Inf Sci Syst. 2022;10(1):21. DOI: https://doi.org/10.1007/s13755-022-00194-8

Xie P, Li Y, Deng B, Du C, Rui S, Deng W, et al. An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer. Int Wound J. 2022;19(4):910-8. DOI: https://doi.org/10.1111/iwj.13691

Zhang J, Qiu Y, Peng L, Zhou Q, Wang Z, Qi M. A comprehensive review of methods based on deep learning for diabetes-related foot ulcers. Front Endocrinol (Lausanne). 2022;13:945020. DOI: https://doi.org/10.3389/fendo.2022.945020

Al-Garaawi N, Ebsim R, Alharan AFH, Yap MH. Diabetic foot ulcer classification using mapped binary patterns and convolutional neural networks. Comput Biol Med. 2022;140:105055. DOI: https://doi.org/10.1016/j.compbiomed.2021.105055

Yogapriya J, Chandran V, Sumithra MG, Elakkiya B, Ebenezer AS, Dhas CSG. Automated detection of infection in diabetic foot ulcer images using convolutional neural network. J Healthc Eng. 2022;2022:2349849. DOI: https://doi.org/10.1155/2022/2349849

Kaselimi M, Protopapadakis E, Doulamis A, Doulamis N. A review of noninvasive sensors and artificial intelligence models for diabetic foot monitoring. Front. Physiol. 2022;13: 924546. DOI: https://doi.org/10.3389/fphys.2022.924546

Hernandez-Guedes A, Santana-Perez I, Arteaga-Marrero N, Fabelo H, Callico GM, Ruiz-Alzola J. Performance evaluation of deep learning models for image classification over small datasets: diabetic foot case study. IEEE Access. 2022;10:124373-86. DOI: https://doi.org/10.1109/ACCESS.2022.3225107

Anggreni NKIS, Kristianto H, Handayani D, Yueniwati Y, Irawan PLT, Rosandi R, et al. Artificial intelligence for diabetic foot screening based on digital image analysis: A systematic review. J Diabetes Sci Technol. 2025;17:19322968251317521. DOI: https://doi.org/10.1177/19322968251317521

Yap MH, Hachiuma R, Alavi A, Brüngel R, Cassidy B, Goyal M, et al. Deep learning in diabetic foot ulcers detection: A comprehensive evaluation. Comput Biol Med. 2021;135:104596. DOI: https://doi.org/10.1016/j.compbiomed.2021.104596

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 (Lausanne). 2021;7:601602. DOI: https://doi.org/10.3389/fmed.2020.601602

Xu Y, Han K, Zhou Y, Wu J, Xie X, Xiang W. Classification of diabetic foot ulcers using class knowledge banks. Front Bioeng Biotechnol. 2022;9:811028. DOI: https://doi.org/10.3389/fbioe.2021.811028

Zhao N, Zhou Q, Hu J, Huang W, Xu J, Qi M, et al. Construction and verification of an intelligent measurement model for diabetic foot ulcer. J. Central South Univ. (Medical Sci). 2021;46(10):1138-46. DOI: https://doi.org/10.11817/j.issn.1672-7347.2021.200938

Lian Z, Wang H, Chen M, Li J. An improved plantar regional division algorithm for aided diagnosis of early diabetic foot. IJPRAI. 2020;34(14):20570062. DOI: https://doi.org/10.1142/S0218001420570062

Kim RB, Gryak J, Mishra A, Cui C, Soroushmehr SMR, Najarian K, et al. Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers. Comput Biol Med. 2020;126:104042. DOI: https://doi.org/10.1016/j.compbiomed.2020.104042

Sequeira, MT. Aplicações de redes neurais convolucionais para a classificação de úlceras de pé diabético. [dissertação]. Porto (PT): Instituto Superior de Engenharia do Porto;2024. Disponível em: https://recipp.ipp.pt/bitstream/10400.22/26725/1/Tese_5624_v2.pdf

Goyal M, Reeves ND, Rajbhandari S, Ahmad N, Wang C, Yap MH. Recognition of ischemia and infection in diabetic foot ulcers: dataset and techniques. Comput. Biol. Med. 2020;117:103616. DOI: https://doi.org/10.1016/j.compbiomed.2020.103616

Thotad PN, Bharamagoudar GR, Anami BS. Diabetic foot ulcer detection using deep learning approaches. Sens Intern. 2023;4:100210. DOI: https://doi.org/10.1016/j.sintl.2022.100210

Chan KS, Lo ZJ. Wound assessment, imaging and monitoring systems in diabetic foot ulcers: A systematic review. Int Wound J. 2020;17(6):1909-23. DOI: https://doi.org/10.1111/iwj.13481

Cui C, Thurnhofer-Hemsi K, Soroushmehr R, Mishra A, Gryak J, Dominguez E, et al. Diabetic wound segmentation using Convolutional Neural Networks. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:1002-05. DOI: https://doi.org/10.1109/EMBC.2019.8856665

Goyal M, Reeves ND, Rajbhandari S, Yap MH. Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices. IEEE J Biomed Health Inform. 2019;23(4):1730-41. DOI: https://doi.org/10.1109/JBHI.2018.2868656

Cruz-Vega I, Peregrina-Barreto H, Rangel-Magdaleno JJ, Ramirez-Cortes JM. A comparison of intelligent classifiers of thermal patterns in diabetic foot. IEEE Xplore. 2019;12:1-6. DOI: https://doi.org/10.1109/I2MTC.2019.8827044

Wang L, Pedersen PC, Agu E, Strong DM, Tulu B. Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification. IEEE Trans Biomed Eng. 2017;64(9):2098-109. DOI: https://doi.org/10.1109/TBME.2016.2632522

Das SK, Roy P, Mishra AK. DFU_SPNet: a stacked parallel convolution layers based CNN to improve diabetic foot ulcer classification. ICT Express. 2022;8(2):271-5. DOI: https://doi.org/10.1016/j.icte.2021.08.022

Liu C, van Netten JJ, van Baal JG, Bus SA, van der Heijden F. Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis. J Biomed Opt. 2015;20(2):26003. DOI: https://doi.org/10.1117/1.JBO.20.2.026003

Vardasca R, Vaz L, Magalhães C, Seixas A, Mendes J. Towards the diabetic foot ulcers classification with infrared thermal images. QIRT. 2018;14:293-6. DOI: https://doi.org/10.21611/qirt.2018.008

Kairys A, Pauliukiene R, Raudonis V, Ceponis J. Towards home-based diabetic foot ulcer monitoring: A systematic review. Sensors. 2023;23:3618. DOI: https://doi.org/10.3390/s23073618

Basiri R, Manji K, LeLievre PM, Toole J, Kim F, Khan SS, et al. Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning. Biomed Eng Online. 2024;23(1):12. DOI: https://doi.org/10.1186/s12938-024-01210-6

Huda MN, Musdholifah A, Frisky AZK. Optimization of plantar foot thermogram for diabetic foot ulceration early detection: An image enhancement approach. STC Journal. 2025;5(2):49-66. DOI: https://doi.org/10.59190/stc.v5i2.273

Goyal M, Yap MH, Reeves ND, Rajbhandari S, Spragg J. Fully convolutional networks for diabetic foot ulcer segmentation. IEEE Xplore. 2017;2017:618-23. DOI: https://doi.org/10.1109/SMC.2017.8122675

Alzubaidi L, Fadhel MA, Oleiwi SR, Al-Shamma O, Zhang J. DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimed Tools Appl. 2020;79:15655–77. DOI: https://doi.org/10.1007/s11042-019-07820-w

Eid MM, Yousef RN, Mohamed MA. A proposed automated system to classify diabetic foot from thermography. IJSER. 2018;9(12):371-81. DOI: https://www.researchgate.net/publication/342164315_A_proposed_Automated_System_to_Classify_Diabetic_Foot_from_Thermography

Sarmun R, Chowdhury MEH, Murugappan M, Aqel A, Ezzuddin M, Rahman SM, et al. Diabetic foot ulcer detection: combining deep learning models for improved localization. Cognit Comput. 2024;16:1413–31. DOI: https://doi.org/10.1007/s12559-024-10267-3

Fraiwan L, AlKhodari M, Ninan J, Mustafa B, Saleh A, Ghazal M. Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study. Biomed Eng Online. 2017;16(1):117. DOI: https://doi.org/10.1186/s12938-017-0408-x

Filipe V, Teixeira P, Teixeira A. Automatic classification of foot thermograms using machine learning techniques. Algorithms. 2022;15(7):236-51. DOI: https://doi.org/10.3390/a15070236

Cajacuri LAV. Early diagnostic of diabetic foot using thermal images [Thesis]. Orléans (FR): Université d'Orléans;2013. DOI: https://theses.hal.science/tel-01022921v1

Liu C, van der Heijden F, Klein ME, van Baal JG, Bus SA, van Netten JJ. Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study. SPIE BiOS. 2013;8572. DOI: https://doi.org/10.1117/12.2001807

Frykberg RG, Gordon IL, Reyzelman AM, Cazzell SM, Fitzgerald RH, Rothenberg GM, et al. Feasibility and efficacy of a smart mat technology to predict development of diabetic plantar ulcers. Diabetes Care. 2017;40(7):973-80. DOI: https://doi.org/10.2337/dc16-2294

Khandakar A, Chowdhury MEH, Ibne Reaz MB, Md Ali SH, Hasan MA, Kiranyaz S, et al. A machine learning model for early detection of diabetic foot using thermogram images. Comput Biol Med. 2021;137:104838. DOI: https://doi.org/10.1016/j.compbiomed.2021.104838

Ahmad M, Tan M, Shalhoub J, Davies A. The use of artificial intelligence in three-dimensional imaging modalities and diabetic foot disease: A systematic review. JVS Vasc Insights. 2024;2:100057. DOI: https://doi.org/10.1016/j.jvsvi.2024.100057

Oliveira ALC, Carvalho AB, Dantas DO. Faster R-CNN approach for diabetic foot ulcer detection. VISAPP. 2021;4:677-84. DOI: https://doi.org/105220/0010255506770684

Gamage HVLC, Wijesinghe WOKIS, Perera I. Instance-based segmentation for boundary detection of neuropathic ulcers through Mask-RCNN. ICANN. 2019;11731:511-22. DOI: https://doi.org/10.1007/978-3-030-30493-5_49

Ferreira ACBH, Ferreira DD, Oliveira HC, Resende IC, Anjos A, Lopes MHBM. Competitive neural layer-based method to identify people with high risk for diabetic foot. Comput Biol Med. 2020;120:103744. DOI: https://doi.org/10.1016/j.compbiomed.2020.103744

Kaabouch N, Chen Y, Anderson J, Ames F, Paulson R. Asymmetry analysis based on genetic algorithms for the prediction of foot ulcers. SPIE. 2009;7243:724304. DOI: https://doi.org/10.1117/12.805975

Alkhalefah S, AlTuraiki I, Altwaijry N. Advancing diabetic foot ulcer care: AI and Generative AI approaches for classification, prediction, segmentation, and detection. Healthcare (Basel). 2025;13(6):648. DOI: https://doi.org/10.3390/healthcare13060648

Persson V, Wickman UL. Artificial intelligence as a tool for self-care in patients with diabetes. Healthcare (Basel). 2025;13(8):950. DOI: https://doi.org/10.3390/healthcare13080950

Mayya V, Kandala RNVPS, Gurupur V, King C, Vu GT, Wan THT. Need for an Artificial Intelligence-based Diabetes Care Platform. Health Serv Res Manag Epidemiol. 2024;11:23333928241275292. DOI: https://doi.org/10.1177/23333928241275292

Foppen RJG, Gioia, V, Gupta S, Johnson CL, Giantsidis J, Papademetris M. Methodology for safe and secure AI in diabetes. J Diabetes Sci Technol. 2024;19(3):620-7. DOI: https://doi.org/10.1177/19322968241304434

Rathore PS, Kumar A, Nandal A, Daca A, Sharma AK. A feature explainability-based deep learning technique for diabetic foot ulcer diagnosis. Sci Rep. 2025. DOI: https://doi.org/10.1038/s41598-025-90780-z

El Arab RA, Al Moosa OA, Abuadas FH, Somerville J. The role of AI in nursing education and practice: umbrella review. J Med Internet Res. 2025;27:e69881. DOI: https://doi.org/10.2196/69881

von Gerich H, Moen H, Block LJ, Chu HC, DeForest H, Hobensack M, et al. Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. Int J Nurs Stud. 2022;127:e104156. DOI: https://doi.org/10.1016/j.ijnurstu.2021.104153

Pereira CLA, Coelho VAT, Nascimento ES, Bigatello CS. Integração da Inteligência Artificial na Enfermagem: avanços, desafios e perspectivas para a prática professional. Id on Line Rev Psic. 2025;19(78):324-36. DOI: https://doi.org/10.14295/idonline.v19i78.4283

Kabir MA, Samad S, Ahmed F, Naher S, Featherston J, Laird C, et al. Mobile apps for wound assessment and monitoring. J Med Syst. 2024;48(1):80. DOI: https://doi.org/10.1007/s10916-024-02091-x

American Nurses Association. The ethical use of artificial intelligence in nursing practice. Silver Spring: ANA. 2022. Available from: https://www.nursingworld.org/globalassets/practiceandpolicy/nursing-excellence/ana-position-statements/the-ethical-use-of-artificial-intelligence-in-nursing-practice_bod-approved-12_20_22.pdf

Vitorino LM, Yoshinari Júnior GH, Lopes-Júnior LC. Inteligência Artificial na Enfermagem: avanços no julgamento clínico e na tomada de decisão. Rev. bras. enferm. 2025;78(4):e780401. DOI: https://doi.org/10.1590/0034-7167.2025780401pt

Publicado
2026-04-22
Como Citar
1.
Kamada RV, Magalhães SR, Fracaroli YR, Fava SMCL, Dázio EM de R, Costa AS, Costa ICP, Gomes LC. Aplicações da inteligência artificial no cuidado com o pé diabético: revisão de escopo. J. nurs. health. [Internet]. 22º de abril de 2026 [citado 2º de maio de 2026];16(1):e1630565. Disponível em: https://periodicos.ufpel.edu.br/index.php/enfermagem/article/view/30565
Seção
Artigos de Revisão