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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1571) Artificial Intelligence (1732) mHealth for Data Collection and Research (1081) mHealth for Diagnosis (69) mHealth for Telemedicine and Homecare (475) mHealth in a Clinical Setting (680)

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 (33) Tweetations (4) Metrics

Journals

  1. Li D, Mathews C, Zamarripa C, Zhang F, Xiao Q. Wound tissue segmentation by computerised image analysis of clinical pressure injury photographs: a pilot study. Journal of Wound Care 2022;31(8):710 View
  2. Eldem H, Ülker E, Yaşar Işıklı O. Encoder–decoder semantic segmentation models for pressure wound images. The Imaging Science Journal 2022;70(2):75 View
  3. Foltynski P, Ladyzynski P. Internet service for wound area measurement using digital planimetry with adaptive calibration and image segmentation with deep convolutional neural networks. Biocybernetics and Biomedical Engineering 2023;43(1):17 View
  4. Dweekat O, Lam S, McGrath L. Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. International Journal of Environmental Research and Public Health 2023;20(1):796 View
  5. Solte D, Storck M. Künstliche Intelligenz in der Therapie chronischer Wunden – Konzepte und Ausblick. Gefässchirurgie 2023;28(1):24 View
  6. Chairat S, Chaichulee S, Dissaneewate T, Wangkulangkul P, Kongpanichakul L. AI-Assisted Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a Smartphone. Healthcare 2023;11(2):273 View
  7. Sollte D, Storck M. Auf das Training kommt es an. ProCare 2023;28(3):18 View
  8. Kairys A, Pauliukiene R, Raudonis V, Ceponis J. Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review. Sensors 2023;23(7):3618 View
  9. Ramirez-GarciaLuna J, Martinez-Jimenez M, Fraser R, Bartlett R, Lorincz A, Liu Z, Saiko G, Berry G. Is my wound infected? A study on the use of hyperspectral imaging to assess wound infection. Frontiers in Medicine 2023;10 View
  10. Fergus P, Chalmers C, Henderson W, Roberts D, Waraich A. Pressure Ulcer Categorization and Reporting in Domiciliary Settings Using Deep Learning and Mobile Devices: A Clinical Trial to Evaluate End-to-End Performance. IEEE Access 2023;11:65138 View
  11. Le D, Pham T. Unveiling the role of artificial intelligence for wound assessment and wound healing prediction. Exploration of Medicine 2023:589 View
  12. Malik H, Idris A, Toha S, Idris I, Daud M, Tokhi M. Deploying Patch-Based Segmentation Pipeline for Fibroblast Cell Images at Varying Magnifications. IEEE Access 2023;11:98171 View
  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 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

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

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