ArticleJournal of medical systems2016
Telemedicine Supported Chronic Wound Tissue Prediction Using Classification Approaches.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time.American journal of translational research · 2024Review
- Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask-R-CNN.Computational and mathematical methods in medicine · 2023Article
- BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm.PeerJ. Computer science · 2021Article
- Observational
- A Qualitative Study of the Barriers and Opportunities for Adoption of Web-Portals for Doctors and Patients in Russia.Journal of medical systems · 2017Article
- Spectral Clustering for Unsupervised Segmentation of Lower Extremity Wound Beds Using Optical Images.Journal of medical systems · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Telemedicine helps to deliver health services electronically to patients with the advancement of communication systems and health informatics. Chronic wound (CW) detection and its healing rate assessment at remote distance is very much difficult due to unavailability of expert doctors. This problem generally affects older ageing people. So there is a need of better assessment facility to the remote people in telemedicine framework. Here we have proposed a CW tissue prediction and diagnosis under telemedicine framework to classify the tissue types using linear discriminant analysis (LDA). The proposed telemedicine based wound tissue prediction (TWTP) model is able to identify wound tissue and correctly predict the wound status with a good degree of accuracy. The overall performance of the proposed wound tissue prediction methodology has been measured based on ground truth images. The proposed methodology will assist the clinicians to take better decision towards diagnosis of CW in terms of quantitative information of three types of tissue composition at low-resource set-up.
Indexed as
Identifiers
26728394What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.