Accurate AI-based Characterisation of Surface Size, Depth and Tissue Composition in Hard-to-Heal Wounds

University Hospital, Linkoeping25 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Difficult to Heal Wounds

Eligibility

Sex: ALLMin age: 19 Years

Medical Language ↔ Plain English

Inclusion Criteria: Older than 18 years, men and women Difficult to heal wounds due to diabetes, VLU; Pressure wounds wound larger than 0.5 cm2 Exclusion Criteria: Exclusion Criteria / Contraindications Use of SeeWound 2 is not intended for, or is contraindicated in, the following situations: Patients 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). Wounds with excessive exudation or infection that prevents adequate visual assessment. Patients 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). Wounds with specific identifiable attributes (e.g. on the face, or containing birthmarks or tattoos) where ethical considerations regarding data handling apply.

Accurate AI-based Characterisation of Surface Size, Depth and Tissue Composition in Hard-to-Heal Wounds

Overview

Conditions

Eligibility

Locations (1)

Outcomes

Primary Outcomes