ARTIficial Intelligence-based Smartphone Application for Skin Cancer Detection

Study Overview
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Brief Summary
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The aim of this project is to assess whether a specific smartphone application (Skinvision App®) can be used as a tool to preselect skin lesions suspicious for skin cancer that require urgent medical advice.

Detailed Description
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Skin cancer is the most frequent cancer diagnosed and its incidence will keep on rising in the next decade. Early detection and treatment are key to improve both morbidity and mortality, and to decrease the cost to society. Persons at risk of developing skin cancer may be subjected to regular checkups. However a considerable number of skin cancers develop in the low-risk general population. Since systematic screening in the general population is not cost-effective, smartphone applications that use inbuilt algorithms are of increasing interest and claim to assist in making a risk assessment in case of concerning skin lesions. Based on previous research, a so-called triage consultation was installed at the policlinic of Ghent University Hospital for patients with 1 to 2 lesions of concern: changing mole, ugly duckling, new mole in adult, rapidly growing lesion or non-healing lesion. Skin cancer detection rate in this setting was at least 13% with 4% melanoma. This is 6 to 8-fold higher than reported by conventional skin cancer screening programs (PMID: 26466155; PMID: 33480073). The reason for this is that a preselection of lesions meeting specific criteria is done. This lesion-directed screening may be a way to make skin cancer screening in the general population (more) cost-effective. In this study we will investigate whether the Skinvision app can function as a preselection tool for lesions for which urgent medical advice is needed. Although this app is CE marked and is already promoted to the public, it's performance and value in daily practice have been insufficiently studied and there is a need for independent research. The 4 main objectives of this study will be: To calculate diagnostic performance of the Skinvision App Calculation of sensitivity and specificity by comparing application risk gradings with a reference standard defined as the histopathological diagnosis or clinical diagnosis in case no biopsy or excision was performed; To determine the repeatability and reproducibility of the Skinvision App Identification of factors that influence the risk analysis of the application, including photographer, type of skin lesion, camera position or lighting conditions; To examine user-experience and confidence concerning the use of medical apps Questionnaire-based evaluation of the user-experience with applications in general, as well as more specific the willingness and confidence to use a skin cancer detection application; To estimate the performance and cost-effectiveness of the Skinvision App in the general population Estimation of the app performance in the general population (estimated prevalence of skin cancer 1%) in terms of missed diagnoses and degree of preselection (positive predictive value).

Official Title
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Clinical Performance and Patient Experience of an Artificial Intelligence-based Smartphone Application (Skinvision ®) in the Early Detection of Skin Cancer: A Cross-Sectional Study in a Real-life Setting.

Conditions
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Skin Cancer

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients with one or two lesions meeting at least one of the following criteria: New mole in an adult (> 18 years old); 'Ugly duckling' sign (i.e. mole that looks different from other moles in the same person) Changing mole (size, color, shape or structure); Rapid growing lesion Non-healing lesion Written informed consent of the patient Exclusion Criteria: Lack of informed consent for study participation

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic performance of the Skinvision application | To evaluate the sensitivity and specificity of the application. The risk assessment of the application will be compared to the gold standard. The gold standard is defined as the histopathologic diagnosis (in biopsied and excised lesions) or clinical assessment by one or two experienced dermatologists. The risk assessment of the application is defined as low (green), medium (orange) or high (red) risk. The biopsied or excised skin lesions will be categorized as benign or malignant. | Up to 24 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Usability and reproducibility of the Skinvision application | To examine the usability and reproducibility of the application. Lesion-specific parameters will be collected (e.g., localization, hair or other disturbing factors, etc). A repeated analysis of one or more specific lesions will be made in different lighting conditions and from different camera positions. Given the evolution of the camera quality, different smartphones will be tested. Finally, the patient will also be asked to perform an analysis to assess the user friendliness. | Up to 24 months | 
| User's acceptability of medical smartphone applications | Patients will be asked about their willingness-to-use medical smartphone applications, including more specifically, a skin cancer detection application. Participants will provide their level of agreement or disagreement for a series of statements with a agree-disagree scale (1 = strongly disagree to 5 = strongly agree). | Day 1 | 
| User's confidence in using smartphone applications for skin cancer detection | Patient's confidence will be scored on a 5-point scale (1 = not confident to 5 = highly confident). Higher scores indicate a greater confidence in the evaluation and risk stratification of suspicious lesions by a skin cancer detection application | Day 1 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Screening, Prevention, Artificial Intelligence, Skinvision App