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Not yet recruiting NCT07619430

AI-Based Wound Monitoring: Automated Wound Progression Assessment Via Marker-Free Image Sequence

— Observational Hard-to-heal Wounds AI (Artificial Intelligence) Wound Care Pressure Injuries

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Marker-free wound image registration technology.
Who it may be relevant to
Registry conditions: Hard-to-heal Wounds, AI (Artificial Intelligence), Wound Care, Pressure Injuries. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Taiwan
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Overview

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.

Detailed description

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.

Interventions

  • Other Marker-free wound image registration technology
    Marker-free longitudinal wound image registration and wound progression assessment using serial wound photographs.

Primary outcome measures

  • Agreement between marker-free and marker-based wound progression indices [Time frame: Up to 6 months]
Secondary outcome measures (1)
  • Minimum image overlap threshold for reliable analysis [Time frame: Up to 6 months]

Eligibility criteria

Inclusion criteria

  • (1) Presence of a hard-to-heal wound that has remained unhealed for more than one month. (2)The wound can be photographed according to the standardized imaging protocol. (3)The participant was aged 18 years or older and provided informed consent personally or via a legally authorized representative, with a signed informed consent form.

Exclusion criteria

  • Wounds whose margins could not be fully included within the imaging field.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

Taiwan · 1 center
  • National Taiwan University Hospital Yunlin branch — Douliu

Publications

  • 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. PMID 36639395
  • 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. PMID 21134035
  • 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. PMID 34544270
  • 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. PMID 29505569
  • 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. PMID 33288961
  • 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. PMID 10759974
  • 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. PMID 32584259
  • 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. PMID 21388743

Identifiers

NCT: NCT07619430 · 202603061RINB

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗