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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.03188 |
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| _version_ | 1866918045578952704 |
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| author | Sikha, Madhu Babu Appari, Lalith Ganesh, Gurudatt Nanjanagudu Bandodkar, Amay Banerjee, Imon |
| author_facet | Sikha, Madhu Babu Appari, Lalith Ganesh, Gurudatt Nanjanagudu Bandodkar, Amay Banerjee, Imon |
| contents | Diabetic foot ulcers (DFUs), a class of chronic wounds, affect ~750,000 individuals every year in the US alone and identifying non-healing DFUs that develop to chronic wounds early can drastically reduce treatment costs and minimize risks of amputation. There is therefore a pressing need for diagnostic tools that can detect non-healing DFUs early. We develop a low cost, multi-analyte 3D printed assays seamlessly integrated on swabs that can identify non-healing DFUs and a Wound Sensor iOS App - an innovative mobile application developed for the controlled acquisition and automated analysis of wound sensor data. By comparing both the original base image (before exposure to the wound) and the wound-exposed image, we developed automated computer vision techniques to compare density changes between the two assay images, which allow us to automatically determine the severity of the wound. The iOS app ensures accurate data collection and presents actionable insights, despite challenges such as variations in camera configurations and ambient conditions. The proposed integrated sensor and iOS app will allow healthcare professionals to monitor wound conditions real-time, track healing progress, and assess critical parameters related to wound care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Analyte, Swab-based Automated Wound Monitor with AI Sikha, Madhu Babu Appari, Lalith Ganesh, Gurudatt Nanjanagudu Bandodkar, Amay Banerjee, Imon Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Diabetic foot ulcers (DFUs), a class of chronic wounds, affect ~750,000 individuals every year in the US alone and identifying non-healing DFUs that develop to chronic wounds early can drastically reduce treatment costs and minimize risks of amputation. There is therefore a pressing need for diagnostic tools that can detect non-healing DFUs early. We develop a low cost, multi-analyte 3D printed assays seamlessly integrated on swabs that can identify non-healing DFUs and a Wound Sensor iOS App - an innovative mobile application developed for the controlled acquisition and automated analysis of wound sensor data. By comparing both the original base image (before exposure to the wound) and the wound-exposed image, we developed automated computer vision techniques to compare density changes between the two assay images, which allow us to automatically determine the severity of the wound. The iOS app ensures accurate data collection and presents actionable insights, despite challenges such as variations in camera configurations and ambient conditions. The proposed integrated sensor and iOS app will allow healthcare professionals to monitor wound conditions real-time, track healing progress, and assess critical parameters related to wound care. |
| title | Multi-Analyte, Swab-based Automated Wound Monitor with AI |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.03188 |