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
.
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
- 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
- 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
- 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
- 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 2024:110496 View
Books/Policy Documents
- Chattree Y, Jain R. Opportunities and Risks in AI for Business Development. View