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Artificial Intelligence Patient App for RDEB SCCs
NCT05843994 · View on ClinicalTrials.gov ↗
Study Summary
In this study, an artificial intelligence model to detect squamous cell carcinomas (SCC) on photos of recessive dystrophic epidermolysis bullosa (RDEB) skin is developed. The ultimate goal is to integrate this model into an app for patients and physicians, to help detect SCCs in RDEB early. SCCs which rapidly metastasize are the main cause of death in adults with RDEB. The earlier an SCC is recognized, the easier it can be removed and the better the outcome. AI leverages computer science to perform tasks that typically require human intelligence and has recently been used to identify skin cancers based on images. We are currently developing an AI approach for early detection of SCC and distinction of malignancy from chronic wounds and other RDEB skin findings. The aim is to create a web application for patients with RDEB to upload images of their skin and get an output as to SCC present/ no SCC. This will be especially valuable for patients with difficult access to medical expertise and those who are hesitant to allow full skin examination at each visit, often because of fear of biopsies. Thus, this project will directly benefit patients by allowing early recognition of SCCs and will empower patients and their families by providing a home use tool. So far, the study team has mainly used professional images (photographs taken in hospital settings by physicians, nurses, and clinical photographers) of both SCCs in RDEB and images of RDEB skin without SCC to develop and train the AI model. The images that are expected in a real-life setting will mostly be pictures taken by patients or family members with their phones or digital cameras. These images have different properties regarding resolution, focus, lighting, and backgrounds. Incorporating such images will be crucial in the upcoming phases of model development-testing and validation-for the web application be a success for patients.
Conditions Studied
Interventions
- OTHER online survey
Study Locations (1)
Illinois
- Department of Dermatology, Northwestern University Feinberg School of Medicine and Lurie Children's Hospital — Chicago
Trial Details
| Field | Value |
|---|---|
| Enrollment Target | 20 participants |
| Start Date | 2023-01-30 |
| Est. Completion | 2028-11-30 |
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Full Details on ClinicalTrials.gov ↗What the Registry Record Tells You About NCT05843994
The ClinicalTrials.gov registry entry for NCT05843994 describes a study currently listed as active not recruiting. It is categorized as an unspecified phase, which is the standard way researchers label where a study sits along the investigational pathway from early safety work through later efficacy and post-marketing evaluation. The registered enrollment target is 20 participants, a figure that helps gauge the scale of data the investigators plan to collect. The listed sponsor is Northwestern University, which has 1,033 total studies on file at ClinicalTrials.gov, and sponsors are the parties responsible for study design, oversight, and regulatory filings.
The record links to 1 condition, with Epidermolysis Bullosa Dystrophica appearing as the primary indexed condition, and to 1 intervention — of which online survey is the first listed. Interventions can include drugs, devices, procedures, behavioral programs, or observational arms, and each is tracked as a separate registry field so that downstream queries can filter accurately. When a trial lists multiple interventions, it usually reflects a multi-arm design or a comparison protocol rather than a single treatment being tested in isolation. The brief summary published in the registry is the clearest source of protocol intent and should be read before drawing conclusions from any sidebar tags.
Geographic footprint matters for practical reasons: NCT05843994 reports 1 study location spanning 1 distinct geographic area — top geographies include Illinois. A larger site network tends to correlate with broader recruitment capacity, but it does not imply anything about study quality, and site-level enrollment status can diverge from the overall registry status shown above. Every data point on this page comes from the public ClinicalTrials.gov dataset and is reproduced here for reference only; it is not a medical recommendation, an endorsement of the sponsor, or an invitation to enroll. Verify current status, eligibility criteria, and contact details directly at ClinicalTrials.gov, and discuss any participation decision with your own healthcare provider.
Frequently Asked Questions
What is clinical trial NCT05843994 about?
NCT05843994 is a clinical study titled "Artificial Intelligence Patient App for RDEB SCCs". In this study, an artificial intelligence model to detect squamous cell carcinomas (SCC) on photos of recessive dystrophic epidermolysis bullosa (RDEB) skin is developed. The ultimate goal is to integrate this model into an app for patients and physicians, to help detect SCCs in RDEB early. SCCs wh...
What is the current status of trial NCT05843994?
This trial is currently active not recruiting. The enrollment target is 20 participants. The study started on 2023-01-30. Estimated completion is 2028-11-30.
What conditions does trial NCT05843994 study?
This clinical trial studies the following conditions: Epidermolysis Bullosa Dystrophica. These conditions were identified from the trial registry and reflect the primary focus areas of the research.
What interventions are being tested in trial NCT05843994?
The interventions under investigation include: online survey (OTHER). Each intervention is being evaluated for safety and efficacy as part of this clinical study.
Who is sponsoring clinical trial NCT05843994?
This trial is sponsored by Northwestern University, which has 1,033 total clinical trials registered on ClinicalTrials.gov. The sponsor is responsible for the study's design, funding, and regulatory compliance.
Where is trial NCT05843994 being conducted?
This trial has 1 study location across Illinois. Contact the study sites directly through ClinicalTrials.gov for enrollment availability.
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