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](https://asset.jmir.pub/assets/984b023e3913985c80dbd105225a35fd.png 480w,https://asset.jmir.pub/assets/984b023e3913985c80dbd105225a35fd.png 960w,https://asset.jmir.pub/assets/984b023e3913985c80dbd105225a35fd.png 1920w,https://asset.jmir.pub/assets/984b023e3913985c80dbd105225a35fd.png 2500w)
Journals
- 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
- 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
- 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
- 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
- Solte D, Storck M. Künstliche Intelligenz in der Therapie chronischer Wunden – Konzepte und Ausblick. Gefässchirurgie 2023;28(1):24 View
- 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
- Sollte D, Storck M. Auf das Training kommt es an. ProCare 2023;28(3):18 View
- Kairys A, Pauliukiene R, Raudonis V, Ceponis J. Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review. Sensors 2023;23(7):3618 View
- 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
- 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
- Le D, Pham T. Unveiling the role of artificial intelligence for wound assessment and wound healing prediction. Exploration of Medicine 2023:589 View
- 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
- 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
- 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
- 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