Hard-to-heal WoundsNCT07619430
AI-Based Wound Monitoring: Automated Wound Progression Assessment Via Marker-Free Image Sequence
An observational study of Hard-to-heal Wounds and AI (Artificial Intelligence), run by National Taiwan University Hospital at 1 site in Taiwan.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hard-to-heal Wounds, AI (Artificial Intelligence), Wound Care
- What is being tested?
- Marker-free wound image registration technology
- How many people
- 1,000 participants
- Where
- 1 siteTaiwan
- Study dates
- 8 Jun 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients with hard-to-heal wounds located on body sites that can be fully captured within a single standardized image.
Who may be able to join
- (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.
Who cannot join
- Wounds whose margins could not be fully included within the imaging field.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
National Taiwan University Hospital Yunlin branch
Douliu, Yunlin, 640
About this study
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.
Read the 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.
- Being tested
- Marker-free wound image registration technology
- Keywords
- chronic wounds, wound assessment, medical AI, semantic segmentation, longitudinal image registration, marker-free, wound progression index, home-based care, telemedicine, clinical validation
- Medical subject headings
- Pressure Ulcer
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Marker-free wound image registration technologyOther
Marker-free longitudinal wound image registration and wound progression assessment using serial wound photographs.
| Study group | Role | What they receive |
|---|---|---|
| patients with hard-to-heal wounds | patients with hard-to-heal wounds Receives: Other: Marker-free wound image registration technology |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to 6 months
Secondary outcomes (1)
Minimum image overlap threshold for reliable analysis
To determine the minimum overlap ratio required to achieve statistically acceptable agreement.
Measured: Up to 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jun 2026
- Study start8 Jun 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Hard-to-heal WoundsStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Industrial Technology Research Institute, Taiwan
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07619430
- Sponsor's protocol ID
- 202603061RINB
- Status last verified
- May 2026
- First submitted
- 27 May 2026
- Last updated
- 2 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: wound images
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.
PubMed 36639395Bowling 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.
PubMed 21134035Anisuzzaman 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.
PubMed 34544270Foltynski 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.
PubMed 29505569Isensee 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.
PubMed 33288961Hallett 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.
PubMed 10759974Chen 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.
PubMed 32584259Bloemen 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.
PubMed 21388743Olsson 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.
PubMed 30362646
Study at a glance
From the registry record, last updated by the sponsor on 2 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Based Wound Monitoring: Automated Wound Progression Assessment Via Marker-Free Image Sequence
- ClinicalTrials.gov ID
- NCT07619430
- Acronym / sponsor ID
- 202603061RINB
- Keywords
- chronic wounds, wound assessment, medical AI, semantic segmentation, longitudinal image registration, marker-free, wound progression index, home-based care
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- Industrial Technology Research Institute, Taiwan
- Study start
- 8 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 2 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patients with hard-to-heal wounds | – | Other: Marker-free wound image registration technologypatients with hard-to-heal wounds |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients with hard-to-heal wounds located on body sites that can be fully captured within a single standardized image.
Key inclusion criteria (1 listed)
- (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.
Key exclusion criteria (1 listed)
- Wounds whose margins could not be fully included within the imaging field.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Marker-free wound image registration technologyMarker-free longitudinal wound image registration and wound progression assessment using serial wound photographs. | Other | patients with hard-to-heal wounds |
Primary outcome
Agreement between marker-free and marker-based wound progression indices
Time frame: Up to 6 months
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.
1 secondary outcome
Minimum image overlap threshold for reliable analysis
Time frame: Up to 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jun 2026.
- Background: PubMed 36639395Liu 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.
- Background: PubMed 21134035Bowling 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.
- Background: PubMed 34544270Anisuzzaman 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.
- Background: PubMed 29505569Foltynski 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.
- Background: PubMed 33288961Isensee 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.
- Background: PubMed 10759974Hallett 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.
- Background: PubMed 32584259Chen 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.
- Background: PubMed 21388743Bloemen 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.
- Background: PubMed 30362646Olsson 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.
- Individual participant dataSharing planned: Yes. wound images
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hard-to-heal Wounds and AI (Artificial Intelligence), run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 2 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Hui-Hsiu Chang (886-972655406, huihsiu@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement between marker-free and marker-based wound progression indices (time frame: Up to 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07619430, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Jun 2026.