Summary of Is Plantar Thermography a Valid Digital Biomarker For Characterising Diabetic Foot Ulceration Risk?, by Akshay Jagadeesh et al.
Is plantar thermography a valid digital biomarker for characterising diabetic foot ulceration risk?
by Akshay Jagadeesh, Chanchanok Aramrat, Aqsha Nur, Poppy Mallinson, Sanjay Kinra
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning paper proposes using plantar thermography to identify individuals at high risk of developing diabetic foot ulcers. By leveraging cross-sectional associations between causal risk factors like peripheral neuropathy and PAD, the authors aim to validate this imaging modality as a tool for DFU risk stratification. The study utilizes machine learning methods and datasets to establish relationships between thermographic features and DFU risk factors. Evaluations are based on established benchmarks, such as accuracy metrics, and focus on the potential applications of plantar thermography in diabetic patient care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study uses a special imaging technique called plantar thermography to help predict who might get a type of foot ulcer that’s common in people with diabetes. Right now, there’s no way to know for sure if someone will get this kind of ulcer until it happens. The researchers are trying to figure out if the imaging test can be used to identify people who are more likely to get this kind of problem. |
Keywords
» Artificial intelligence » Machine learning
