This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1732) Artificial Intelligence (2129) mHealth for Data Collection and Research (1196) mHealth for Diagnosis (81) mHealth for Telemedicine and Homecare (531) mHealth in a Clinical Setting (743)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36977, first published .
Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study

Authors of this article:

Dhanesh Ramachandram1 Author Orcid Image ;   Jose Luis Ramirez-GarciaLuna1 Author Orcid Image ;   Robert D J Fraser1 Author Orcid Image ;   Mario Aurelio Martínez-Jiménez2 Author Orcid Image ;   Jesus E Arriaga-Caballero2 Author Orcid Image ;   Justin Allport1 Author Orcid Image
Article Authors Cited by (54) Tweetations (4) Metrics

Journals

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  7. Sollte D, Storck M. Auf das Training kommt es an. ProCare 2023;28(3):18 View
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  13. Gupta R, Goldstone L, Eisen S, Ramachandram D, Cassata A, Fraser R, Ramirez-GarciaLuna J, Bartlett R, Allport J. Towards an AI-Based Objective Prognostic Model for Quantifying Wound Healing. IEEE Journal of Biomedical and Health Informatics 2024;28(2):666 View
  14. Dhar M, Zhang T, Patel Y, Gopalakrishnan S, Yu Z. FUSegNet: A deep convolutional neural network for foot ulcer segmentation. Biomedical Signal Processing and Control 2024;92:106057 View
  15. Georg P, Schmid M, Zahia S, Probst S, Cazzaniga S, Hunger R, Bossart S. Evaluation of a Semi-Automated Wound-Halving Algorithm for Split-Wound Design Studies: A Step towards Enhanced Wound-Healing Assessment. Journal of Clinical Medicine 2024;13(12):3599 View
  16. Chen M. Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time. American Journal of Translational Research 2024;16(7):2765 View
  17. Prakashan D, Kaushik A, Gandhi S. Smart sensors and wound dressings: Artificial intelligence-supported chronic skin monitoring – A review. Chemical Engineering Journal 2024;497:154371 View
  18. Raja M, Pannirselvam V, Srinivasan S, Guhan B, Rayan F. Recent technological advancements in Artificial Intelligence for orthopaedic wound management. Journal of Clinical Orthopaedics and Trauma 2024;57:102561 View
  19. Deng J, Shi G, Ye Z, Xiao Q, Zhang X, Ren L, Yang F, Wang M. Unveiling and swift diagnosing chronic wound healing with artificial intelligence assistance. Chinese Chemical Letters 2025;36(3):110496 View
  20. Griffa D, Natale A, Merli Y, Starace M, Curti N, Mussi M, Castellani G, Melandri D, Piraccini B, Zengarini C. Artificial Intelligence in Wound Care: A Narrative Review of the Currently Available Mobile Apps for Automatic Ulcer Segmentation. BioMedInformatics 2024;4(4):2321 View
  21. Лукащук Б, Шабатура Ю. Methodology for evaluating complex object contour detection accuracy in SLIC-based image segmentation. Scientific Bulletin of UNFU 2024;34(8) View
  22. Zhang Z, Sheng S, Zhu S, Jin J. Chronic Wound Assessment System Using an Improved UPerNet Model. International Journal of Imaging Systems and Technology 2025;35(1) View
  23. Reifs Jiménez D, Casanova-Lozano L, Grau-Carrión S, Reig-Bolaño R. Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review. Journal of Medical Systems 2025;49(1) View
  24. Mohammed H, Corcoran K, Lavergne K, Graham A, Gill D, Jones K, Singal S, Krishnamoorthy M, Cassata A, Mannion D, Fraser R. Clinical, Operational, and Economic Benefits of a Digitally Enabled Wound Care Program in Home Health: Quasi-Experimental, Pre-Post Comparative Study. JMIR Nursing 2025;8:e71535 View
  25. Blake H. Digital wound management: how it works and its potential benefits in wound care practice. Nursing Standard 2025;40(6):61 View
  26. Cassidy B, McBride C, Kendrick C, Reeves N, Pappachan J, Fernandez C, Chacko E, Brüngel R, Friedrich C, Alotaibi M, AlWabel A, Alderwish M, Lai K, Yap M. An enhanced harmonic densely connected hybrid transformer network architecture for chronic wound segmentation utilising multi-colour space tensor merging. Computers in Biology and Medicine 2025;192:110172 View
  27. Mohammed H, Bestavros S, Mohsen S, Liu Z, Wang S, Allport J, Cassata A, Fraser R. Assessing Clinician Consistency in Wound Tissue Classification and the Value of AI‐Assisted Quantification: A Cross‐Sectional Study. International Wound Journal 2025;22(6) View
  28. Morgado A, Carvalho R, Sampaio A, Vasconcelos M. Enhancing chronic wound assessment through agreement analysis and tissue segmentation. Scientific Reports 2025;15(1) View
  29. Majjouti K, Priester V, Tapp-Herrenbrueck M, Brehmer A, Pinnekamp H, Aleithe M, Fischer U, Kleesiek J, Hosters B. Nursing-centered development of an AI-based decision support system in pressure ulcer and incontinence-associated dermatitis management - a mixed methods study. BMC Nursing 2025;24(1) View
  30. Kücking F, Hübner U, Busch D. Diagnostic accuracy differences in detecting wound maceration between humans and artificial intelligence: the role of human expertise revisited. Journal of the American Medical Informatics Association 2025;32(9):1425 View
  31. Kwarciak K, Daniol M, Hemmerling D, Wodzinski M. Unsupervised skull segmentation in MR images utilizing modality translation and super-resolution. Scientific Reports 2025;15(1) View
  32. Stefanelli A, Zahia S, Chanel G, Niri R, Pichon S, Probst S. Developing an AI-powered wound assessment tool: a methodological approach to data collection and model optimization. BMC Medical Informatics and Decision Making 2025;25(1) View
  33. Lindborg K, Karlsson M, Kotorri A, Sjöberg F, Fredrikson M, Haglind A, Sjöberg Z, Elmasry M. Accurate AI-Based Characterization of Wound Size and Tissue Composition in Hard-to-Heal Wounds. Journal of Clinical Medicine 2025;14(16):5838 View
  34. Talukder Showrav T, Zubair Hasan M, Hasan M. DFUSegNet: Boundary-aware hierarchical attentive fusion network with adaptive preprocessing for diabetic foot ulcer segmentation. Knowledge-Based Systems 2025;329:114323 View
  35. Cassidy B, Kendrick C, Reeves N, Pappachan J, Yap M. Deep learning in chronic wound segmentation: a comprehensive review and meta-analysis. The Visual Computer 2025;41(14):11885 View
  36. Cai X, Li W, Shi W, Cai Y, Zhou J. Modeling epithelial wound closure dynamics with AI: A comparative study across cell types. Regenerative Therapy 2025;30:860 View
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  38. Wei Y, Liu X, Pei J, Zhang H, Han L. Artificial Intelligence in Pressure Injury Diagnosis: A Critical Appraisal for Clinical Practice. Advances in Wound Care 2025 View
  39. Li X, Luo H, Liu X, Zhang T, Zhang M. Novel wound image segmentation with enhanced global context and Adaptive Channel-Aware Normalization. Expert Systems with Applications 2026;302:130607 View

Books/Policy Documents

  1. Chattree Y, Jain R. Opportunities and Risks in AI for Business Development. View
  2. Das S, Chaudhuri R, Deb S. Computing and Machine Learning. View
  3. Ghahremani S. Structural, Syntactic, and Statistical Pattern Recognition. View
  4. Carvalho R, Morgado A, Sampaio A, Vasconcelos M. Applications of Medical Artificial Intelligence. View
  5. Sarkar S, Das S, Chanda A, Biswas S. Explainable and Responsible Artificial Intelligence in Healthcare. View
  6. Bazargani M, Heidari M, Anvari-Fard M, Soltanian-Zadeh H. Joint 20th Nordic-Baltic Conference on Biomedical Engineering & 24th Polish Conference on Biocybernetics and Biomedical Engineering. View
  7. Potrzebowski P, Karbowski A, Sterniuk K, Krajna A, Cemka Z, Opała K, Rumiński J. Joint 20th Nordic-Baltic Conference on Biomedical Engineering & 24th Polish Conference on Biocybernetics and Biomedical Engineering. View
  8. Miladinović A, Biscontin A, Bonini A, Bassi F, Kresevic S, Raffini A, Iscra K, Accardo A, Ajčević M. Joint 20th Nordic-Baltic Conference on Biomedical Engineering & 24th Polish Conference on Biocybernetics and Biomedical Engineering. View
  9. Sampaio A, Carvalho R, Vasconcelos M. Medical Image Understanding and Analysis. View
  10. Borst V, Dittus T, Dege T, Schmieder A, Kounev S. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. View
  11. Majee S, Singh S, Das U, Avlani D, Bera R, Karmakar S, Vinchurkar K. Precision 3D Printing in Pharmaceutical Sciences. View

Conference Proceedings

  1. Kuo S, Huang P, Lin C, Li J, Chang M. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation Strategies View
  2. Antunović A, Nyarko E, Filko D. 2024 International Conference on Smart Systems and Technologies (SST). Wound Tissue Classification: A Comparative Analysis of Deep Neural Network Models View
  3. Carvalho R, Sampaio A, Vasconcelos M. 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). Automating Tissue Segmentation and Quantification for Wound Healing Assessment View
  4. Soleh O, Hendry H, Sembiring I, Sutedja I, Jonas D, Kiantara R. 2025 4th International Conference on Creative Communication and Innovative Technology (ICCIT). Explainable CNN Model for Real Time Diabetic Wound Severity Classification in Telemedicine View