Diabetic Foot Ulcer, Diabetic Foot Ulcer (DFU)
Conditions
Keywords
DFU, Monitoring
Brief summary
The goal of this observational pilot study is to evaluate whether a new remote monitoring platform (TASHA) can support accurate and reliable assessment of diabetic foot ulcers in adult patients receiving care in a UK NHS clinic. The main questions it aims to answer are: Can the TASHA platform provide ulcer assessment data (e.g., size, depth, visible infection) that aligns with in-clinic evaluations by healthcare professionals? Can patients accurately perform self-scanning of their ulcers using the platform under clinical supervision? What is the variation (inc. root mean square error) between clinician-led and patient-led scans? Participants will: * Attend a standard in-clinic ulcer assessment as part of their usual care Undergo an additional 3D foot scan using the TASHA platform during their clinic visit. * Be guided to perform a self-scan using the same platform, with clinical staff present. * Have their scan data reviewed remotely by a clinician to compare with in-clinic assessments. * This feasibility study will inform the design of future larger-scale trials and contribute to regulatory development of the TASHA platform as a digital medical device.
Detailed description
This is a prospective, single-site, single-arm pilot study designed to evaluate the feasibility, usability, and technical performance of the Tissue Analytics System for Health Assessments (TASHA) platform, a novel investigational medical device developed by Medical Intelligence Group Ltd. The platform integrates a non-invasive, high-resolution 3D scanning module with a secure data interface to generate a digital twin of diabetic foot ulcers (DFUs), allowing clinicians to assess wound status remotely and, in future iterations, support patient-led ulcer self-monitoring. Study Rationale Diabetic foot ulcers are among the most debilitating and costly complications of diabetes, associated with high risks of infection, hospitalisation, and lower-limb amputation. Early and continuous monitoring of DFUs is critical to guide treatment, detect deterioration, and evaluate healing progression. However, current models of care rely predominantly on in-clinic assessments that are often infrequent due to healthcare capacity limitations, geographic access barriers, or logistical constraints. Remote monitoring has the potential to increase continuity of care, improve early detection of wound deterioration, and reduce the clinical burden on specialist foot care services. The TASHA platform aims to address these needs by enabling structured, repeatable capture of wound images and clinical metadata for remote review and longitudinal tracking. Study Design This is a non-randomised, observational, exploratory pilot study involving up to 30 adult participants with active diabetic foot ulcers, conducted at a single National Health Service (NHS) clinic in the United Kingdom. The study is not powered to demonstrate clinical efficacy but is instead focused on evaluating: Technical performance and image quality Accuracy and consistency of ulcer assessments using TASHA data Feasibility of supervised patient self-scanning Inter-rater reliability between in-clinic and remote clinical reviewers Study Procedures Following informed consent, eligible participants will attend a routine diabetic foot clinic appointment, during which the following study-related activities will occur: Standard Clinical Assessment: Performed by an NHS-employed healthcare professional. Ulcer stage, size, depth, presence of infection, and wound condition will be assessed and recorded according to standard local practice. Clinician-led TASHA Scan: The healthcare professional will perform a 3D scan of the ulcer using the TASHA device. The system uses structured light and depth-sensing to construct a digital model of the ulcer and surrounding tissue, with accompanying photographic documentation. Patient Self-Scan: Participants will then be guided to perform a self-scan of their ulcer using the same TASHA platform, under clinical supervision. This is designed to evaluate usability, scan quality, and the potential for future unsupervised home-based scanning. Remote Clinical Review: TASHA scan data will be de-identified and reviewed by a remote clinician who is blinded to the in-clinic findings. They will assess wound characteristics using a structured form equivalent to the in-person evaluation. Optional Feedback Collection: Participants may optionally provide structured feedback regarding the ease of use, instructions clarity, and perceived difficulty of the scanning task. Device Description TASHA consists of: A 3D scanning unit utilising structured light or depth-sensing technology A software platform for scan acquisition, data encryption, and transfer A secure clinician portal for visualising digital twins and conducting remote assessments The device is currently unmarked (not yet UK Conformity Assessed \[UKCA\] or Conformité Européenne \[CE\] certified) and is being evaluated solely under ethics-approved investigational use conditions. Data Management and Analysis All identifiable data will be securely stored on encrypted NHS infrastructure and will not be accessed by the device developer. Anonymised data (including wound scans and structured assessment metadata) will be securely transferred to Medical Intelligence Group Ltd for performance evaluation and iterative development of the TASHA platform. Data Handling: Role-based access control and encryption will be implemented across all data storage systems Anonymisation will include removal of all identifiers prior to data export Data will not be shared, sold, or commercialised beyond internal research and regulatory preparation Primary data outputs will include ulcer size, surface area, depth estimation, infection markers (as visible), and scan metadata. Planned Statistical Analysis Concordance analysis: Agreement between in-clinic and remote assessments will be assessed using Cohen's Kappa for categorical variables (e.g. ulcer stage) and Intraclass Correlation Coefficients (ICC) or Bland-Altman plots for continuous measures (e.g. ulcer size, depth). Root Mean Square Error (RMSE): RMSE will be used to quantify measurement error between clinician and patient-generated scans, particularly for spatial metrics like ulcer area and volume. Sensitivity and Specificity: The platform's ability to detect changes in ulcer condition (improvement, worsening, stability) will be calculated against in-clinic assessments as the reference standard. Inter-rater Reliability: Multiple clinician reviewers (in-person and remote) will be compared to assess consistency in interpretation of TASHA scan data. Descriptive Statistics: Demographics, ulcer characteristics, scan duration, and usability metrics (e.g. assistance required) will be summarised using basic descriptive statistics. This analysis will inform future statistical powering and endpoint selection for a definitive trial. Ethical and Regulatory Compliance This study has received ethics approval from a UK NHS Research Ethics Committee via the Integrated Research Application System (IRAS). It will be conducted in compliance with the UK Policy Framework for Health and Social Care Research, the Declaration of Helsinki, and Good Clinical Practice (GCP) guidelines. Risk management and insurance: NHS indemnity applies to any clinical care-related activity carried out by NHS-employed staff Medical Intelligence Group Ltd has secured commercial liability insurance for all device-related research risks and data processing activity No adverse events are anticipated, as the TASHA system is non-invasive and used solely to supplement standard care assessments. Future Applications Insights from this pilot study will be used to: Validate the clinical utility of the TASHA platform Optimise usability features for patient-led scanning Support UKCA/CE marking and regulatory submissions Design a statistically powered, multi-site clinical investigation Enable remote wound monitoring capabilities within community and home settings
Interventions
OTHERFoot scan
This does not constitute a intervention but all participants will undergo image scans of their ulcer
Sponsors
Tameside Hospital NHS Foundation Trust
Medical Intelligence Group
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults aged 18 years or older * Diagnosed with a diabetic foot ulcer at the time of recruitment * Receiving routine care at the participating NHS clinic * Able to provide informed consent * Able to sit and engage with the TASHA scanning device * Willing to attempt a supervised self-scan of the affected foot
Exclusion criteria
* Presence of non-diabetic foot ulcers * Severe cognitive or physical impairments that prevent scanning or understanding instructions * Active infection or medical instability requiring urgent intervention * Participation in another interventional study that may interfere with this assessment * Inability to communicate in English (unless appropriate translation or support is available)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Agreement in Ulcer Stage Classification Between Remote and In-Clinic Assessments | Each clinical visit per patient unto 24 weeks | Assessment of agreement in diabetic foot ulcer (DFU) stage classification between in-clinic assessments by healthcare professionals and remote assessments based on TASHA platform scan data. Classification will use a standard ulcer staging system (e.g., Wagner or SINBAD). Agreement will be measured using Cohen's Kappa coefficient to evaluate inter-rater reliability for categorical data. This will determine the platform's ability to support accurate remote clinical interpretation. Unit of Measure: Cohen's Kappa coefficient |
| Agreement in Ulcer Surface Area Measurement Between Remote and In-Clinic Assessments | At each clinical visit per participant, up to 24 weeks | Comparison of ulcer surface area (cm²) between in-clinic manual measurement and remote assessments using TASHA platform 3D scans. Agreement will be evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman analysis to determine alignment between manual and digital assessments. This will help establish whether remote scanning can reliably replicate clinical measurements of DFU size. Unit of measure - Ulcer surface area (cm²) |
| Agreement in Ulcer Depth Measurement Between Remote and In-Clinic Assessments | At each clinical visit per participant, up to 24 weeks | Comparison of ulcer depth (mm) assessed in person by clinicians versus measurements obtained from TASHA platform-generated 3D scans. Statistical agreement will be analysed using Intraclass Correlation Coefficient (ICC) and root mean square error (RMSE). The goal is to determine the level of accuracy and consistency between traditional manual depth estimates and platform-generated data. Unit of measurement - Ulcer depth (mm) |
| Agreement in Detection of Visible Signs of Infection Between Remote and In-Clinic Assessments | At each clinical visit per participant, up to 24 weeks | Comparison of in-clinic and remote assessments in identifying visible infection indicators such as erythema, exudate, and swelling using TASHA platform images. Agreement will be measured using percentage agreement and Cohen's Kappa for categorical variables. This will inform whether remote imaging data supports consistent clinical detection of surface-level infection features. Measured in Percentage agreement / Cohen's Kappa coefficient |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Root Mean Square Error Between Clinician and Patient Self-Scans of Ulcer Surface Area | At each clinical visit per participant, up to 24 weeks | To evaluate the difference in ulcer surface area measurements (cm²) between clinician-performed and patient-performed TASHA scans. RMSE will be used to quantify error and variability between the two scan types. This will help assess the accuracy and feasibility of supervised self-scanning in a clinical setting and inform future deployment of remote self-monitoring workflows. Unit of measure - RMSE (cm²) |
Countries
United Kingdom
Contacts
Primary ContactSagar Jadeja, BDS, PhD, MFDS, AKC
Backup ContactDivia Bhatnagar, BSc, MEng
Outcome results
None listed