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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1065) Artificial Intelligence (709) mHealth for Data Collection and Research (699) mHealth for Diagnosis (31) mHealth for Telemedicine and Homecare (263) mHealth in a Clinical Setting (499)

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 Ramachandram 1 Author Orcid Image ;   Jose Luis Ramirez-GarciaLuna 1 Author Orcid Image ;   Robert D J Fraser 1 Author Orcid Image ;   Mario Aurelio Martínez-Jiménez 2 Author Orcid Image ;   Jesus E Arriaga-Caballero 2 Author Orcid Image ;   Justin Allport 1 Author Orcid Image
Article Authors Cited by (8) Tweetations (5) 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
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  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