Observational studyScientific reports2024
Clinically validated classification of chronic wounds method with memristor-based cellular neural network.
Observational study in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Hyaluronan-Based Hybrid Systems as Growth Factor Carriers in the Treatment of Chronic Wounds.International journal of molecular sciences · 2025Review
- Immunomodulatory Mechanisms of Chronic Wound Healing: Translational and Clinical Relevance.MedComm · 2025Review
- Chronic Ulcers Healing Prediction through Machine Learning Approaches: Preliminary Results on Diabetic Foot Ulcers Case Study.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of developing new techniques and technologies that can help clinicians to monitor the affected patients easily and reliably. In this prospective observational study a new device, the Wound Viewer, that works through a memristor-based Discrete-Time Cellular Neural Network (DT-CNN) has been developed and tested through a clinical trial of 150 patients. The WV has been developed to serve as the state-of-art tool, capable to return the actual clinical information that is most needed by the caregivers: through the WBP scale, it classifies four classes of wounds by the type of tissue: A-only granular tissue; B-<50% slough; C->50% slough; D-necrosis. This work aims to describe in depth the technology and the computational techniques that have been implemented, and to demonstrate reliability in automatically identifying, classifying through internationally accepted clinical scales and measuring such wounds, that peaked to over a 90% of accuracy.
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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.