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| Name | Class |
|---|---|
| Industrial Technology Research Institute, Taiwan | OTHER |
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This study aims to develop a low-cost, marker-free intelligent wound assessment system that can analyze wound photos taken with a standard smartphone. By comparing wound images over time, the system will generate a quantifiable Wound Progression Index (WPI) to provide objective feedback on whether a wound is improving, stable, or worsening. The long-term goal is to support early detection of wound deterioration and improve wound care in both clinical and home settings.
Participants receiving routine wound care will undergo standardized wound photography during dressing changes. Longitudinal wound images will be analyzed to develop and validate a marker-free wound progression assessment method. No treatment assignment or modification of standard clinical care will occur.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| patients with hard-to-heal wounds | patients with hard-to-heal wounds |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Marker-free wound image registration technology | Other | Marker-free longitudinal wound image registration and wound progression assessment using serial wound photographs. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Agreement between marker-free and marker-based wound progression indices | To evaluate the agreement between the Marker-Free Wound Progression Index (WPI_MF) and the gold-standard marker-based Wound Progression Index (WPI_GS) derived from longitudinal wound photographs. | Up to 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Minimum image overlap threshold for reliable analysis | To determine the minimum overlap ratio required to achieve statistically acceptable agreement. | Up to 6 months |
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Inclusion Criteria:
Exclusion Criteria:
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Adult patients with hard-to-heal wounds located on body sites that can be fully captured within a single standardized image.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Hui-Hsiu Chang | Contact | 886-972655406 | huihsiu@gmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Taiwan University Hospital Yunlin branch | Douliu | Yunlin | 640 | Taiwan |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36639395 | Background | Liu TJ, Wang H, Christian M, Chang CW, Lai F, Tai HC. Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera. Sci Rep. 2023 Jan 13;13(1):680. doi: 10.1038/s41598-022-26812-9. | |
| 21134035 | Background | Bowling FL, King L, Paterson JA, Hu J, Lipsky BA, Matthews DR, Boulton AJ. Remote assessment of diabetic foot ulcers using a novel wound imaging system. Wound Repair Regen. 2011 Jan-Feb;19(1):25-30. doi: 10.1111/j.1524-475X.2010.00645.x. Epub 2010 Dec 6. |
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wound images
starting 6 months after publication
Requests must include a structured research proposal specifying the objectives, analysis plan, and data required. All requests will be reviewed by the corresponding investigator and the study's principal research team to ensure scientific validity, ethical compliance, and protection of participant confidentiality. Additional approval from an institutional review board or ethics committee may be required depending on the nature of the proposed analysis.
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| 34544270 | Background | Anisuzzaman DM, Wang C, Rostami B, Gopalakrishnan S, Niezgoda J, Yu Z. Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review. Adv Wound Care (New Rochelle). 2022 Dec;11(12):687-709. doi: 10.1089/wound.2021.0091. Epub 2021 Dec 20. |
| 29505569 | Background | Foltynski P. Ways to increase precision and accuracy of wound area measurement using smart devices: Advanced app Planimator. PLoS One. 2018 Mar 5;13(3):e0192485. doi: 10.1371/journal.pone.0192485. eCollection 2018. |
| 33288961 | Background | Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021 Feb;18(2):203-211. doi: 10.1038/s41592-020-01008-z. Epub 2020 Dec 7. |
| 10759974 | Background | Hallett CE, Austin L, Caress A, Luker KA. Wound care in the community setting: clinical decision making in context. J Adv Nurs. 2000 Apr;31(4):783-93. doi: 10.1046/j.1365-2648.2000.01348.x. |
| 32584259 | Background | Chen L, Cheng L, Gao W, Chen D, Wang C, Ran X. Telemedicine in Chronic Wound Management: Systematic Review And Meta-Analysis. JMIR Mhealth Uhealth. 2020 Jun 25;8(6):e15574. doi: 10.2196/15574. |
| 21388743 | Background | Bloemen MC, van Zuijlen PP, Middelkoop E. Reliability of subjective wound assessment. Burns. 2011 Jun;37(4):566-71. doi: 10.1016/j.burns.2011.02.004. Epub 2011 Mar 8. |
| 30362646 | Background | Olsson M, Jarbrink K, Divakar U, Bajpai R, Upton Z, Schmidtchen A, Car J. The humanistic and economic burden of chronic wounds: A systematic review. Wound Repair Regen. 2019 Jan;27(1):114-125. doi: 10.1111/wrr.12683. Epub 2018 Dec 2. |
| ID | Term |
|---|---|
| D003668 | Pressure Ulcer |
| ID | Term |
|---|---|
| D012883 | Skin Ulcer |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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