Clinically validated classification of chronic wounds method with memristor-based cellular neural network
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-81521-9 |
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| Summary: | 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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| ISSN: | 2045-2322 |