Artificial Intelligence Patient App for RDEB SCCs

Study Overview
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Brief Summary
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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.

Detailed Description
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This project will enroll adolescents and adults with RDEB and history of at least one SCC. The survey and consents will be provided in English, Spanish, German, French, Arabic, Chinese, and Russian. The study team is inviting people with RDEB around the world to participate and are hoping that approximately 100 people will provide images. Participants will be asked to complete the survey and upload photographs of SCC(s) using the links below. Depending on the number of SCCs they have had and the number of photos they want to provide, the survey will take approximately 15-20 minutes to complete. To participate in this study, please follow this link: https://redcap.nubic.northwestern.edu/redcap/surveys/?s=JH9LHR4CC4R4H3HN

Official Title
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Developing a Novel Artificial Intelligence Patient App to Recognize Squamous Cell Carcinoma (SCCs) in Recessive Dystrophic Epidermolysis Bullosa (RDEB): Image Collection

Conditions
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Epidermolysis Bullosa Dystrophica

Intervention / Treatment
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* Other: online survey


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: patient with recessive dystrophic epidermolysis bullosa patient with history of cutaneous squamous cell carcinoma patient consent for upload and use of clinical data and photographs Exclusion Criteria: Patients who do not agree to upload and use of photographs and clinical data

Ages Eligible for Study
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Minimum Age: 12 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: online survey|Participants will complete an online survey and submit photographs.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Percent agreement of the presence or absence of squamous cell carcinoma (SCC) on the skin in photographs as detected by the App versus confirmed physician diagnosis |  | one day survey | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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epidermolysis bullosa, skin abnormalities, congenital abnormalities, skin disease, genetic, genetic diseases, inborn, skin diseases, skin disease, vesiculobullous, carcinoma, squamous cell, skin neoplasms, artificial intelligence