Ignite Creation Date:
2025-12-24 @ 2:22 PM
Ignite Modification Date:
2026-01-19 @ 5:48 PM
Study NCT ID:
NCT07211295
Status:
COMPLETED
Last Update Posted:
2025-10-07
First Post:
2025-08-31
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Accurate AI-based Characterisation of Surface Size, Depth and Tissue Composition in Hard-to-Heal Wounds
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D014456', 'term': 'Ulcer'}], 'ancestors': [{'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 25}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2025-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2025-09-18', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-09-29', 'studyFirstSubmitDate': '2025-08-31', 'studyFirstSubmitQcDate': '2025-09-29', 'lastUpdatePostDateStruct': {'date': '2025-10-07', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-10-07', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2025-09-18', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy', 'timeFrame': 'During the study', 'description': 'Regular outcome measures for a Medical Device'}, {'measure': 'Accurac, Precision, Mean absolute Error (MAE); Coefficient of Variation (CV); SD', 'timeFrame': 'During the study', 'description': 'Regular outcome measures for a Medical Device'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['VLU; difficult to heal wounds; diabete; ulcers;'], 'conditions': ['Difficult to Heal Wounds']}, 'descriptionModule': {'briefSummary': 'This study aims to determine and evaluate the clinical accuracy, precision, and safety of SeeWound 2, an AI-driven wound assessment application, designed for the measurement of wound surface area (cm²), wound depth (mm), and the estimation of the proportion of fibrin covering (slough) and necrosis (%) in real-world clinical settings for patients with hard-to-heal wounds. The study also seeks to validate the non-invasive method for measuring wound depth, as current standard care involves invasive probing of the wound to estimate depth - a practice that this investigational device is intended to replace with a digital, contact-free measurement approach.', 'detailedDescription': "SeeWound 2 is a software-based medical device that utilises artificial intelligence to classify and quantify wound tissue types, specifically fibrin covering (slough) and necrosis, as well as to measure wound surface area and depth through digital image analysis. The system operates as a mobile camera-based application, whereby healthcare professionals capture an image of a hard-to-heal wound. The software then automatically analyses the image using integrated AI models in combination with the LiDAR sensor technology embedded in the mobile camera hardware. The product's capability to automatically measure wound surface area, estimate wound depth in a non-invasive manner, and objectively quantify the proportion of slough and necrosis within the wound bed represents a novel functionality not currently available in clinical practice."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '19 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'ntended Use / Target Population The intended target population for SeeWound 2 comprises patients with hard-to-heal wounds. The device is designed for use by qualified healthcare professionals for the clinical assessment, documentation, and monitoring of chronic and complex wounds, including but not limited to venous leg ulcers, diabetic foot ulcers, and pressure ulcers. SeeWound 2 is intended to support clinical decision-making by providing objective, non-invasive measurements of wound surface area and depth, and by enabling the classification and quantification of wound bed tissue composition (slough and necrosis). The product is not intended to replace clinical judgement but to serve as an adjunctive tool to standard wound care practice.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\nOlder than 18 years, men and women Difficult to heal wounds due to diabetes, VLU; Pressure wounds wound larger than 0.5 cm2\n\nExclusion Criteria:\n\nExclusion Criteria / Contraindications\n\nUse of SeeWound 2 is not intended for, or is contraindicated in, the following situations:\n\nPatients under 18 years of age. Wounds located in anatomical regions where the full wound surface and depth cannot be captured (e.g. deep fistulas, tunnels, or undermined areas not accessible through surface imaging).\n\nWounds with excessive exudation or infection that prevents adequate visual assessment.\n\nPatients who are unable or unwilling to provide informed consent. Patients with a known allergy or adverse reaction to any component used in the image acquisition process (where applicable).\n\nWounds with specific identifiable attributes (e.g. on the face, or containing birthmarks or tattoos) where ethical considerations regarding data handling apply.'}, 'identificationModule': {'nctId': 'NCT07211295', 'acronym': 'SeeWound2', 'briefTitle': 'Accurate AI-based Characterisation of Surface Size, Depth and Tissue Composition in Hard-to-Heal Wounds', 'organization': {'class': 'OTHER', 'fullName': 'University Hospital, Linkoeping'}, 'officialTitle': 'Accurate AI-based Characterisation of Surface Size, Depth and Tissue Composition in Hard-to-Hea Woundsl', 'orgStudyIdInfo': {'id': 'CIP-MDR-SWD001-V1.0-2025, ver,'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Cohort', 'description': 'cohort'}]}, 'contactsLocationsModule': {'locations': [{'zip': '58185', 'city': 'Linköping', 'state': 'Östergötland County', 'country': 'Sweden', 'facility': 'BRIVA, Intensive Care for Burns,', 'geoPoint': {'lat': 58.41086, 'lon': 15.62157}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University Hospital, Linkoeping', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Prof', 'investigatorFullName': 'Folke Sjoberg', 'investigatorAffiliation': 'University Hospital, Linkoeping'}}}}