ARTIficial Intelligence-based Smartphone Application for Skin Cancer Detection (ARTIS)

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.

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.

Study Overview

• Status: Recruiting
• Conditions: Skin Cancer

Detailed Description

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:

1. 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;
2. 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;
3. 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;
4. 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).

• Study Type: Observational
• Enrollment (Estimated): 2500

Contacts and Locations

• Study Contact: Name: Amber Shen, MHP; Phone Number: +3293322243; Email: amber.shen@uzgent.be
• Study Contact Backup: Name: Julie Kips, MD; Phone Number: +32487519041; Email: julie.kips@ugent.be

Study Locations

• Belgium
  • East Flanders
    • Ghent, East Flanders, Belgium, 9000
      • Recruiting
      • Department of Dermatology, Ghent University Hospital
      • Principal Investigator: Lieve Brochez, MD, PhD
      • Sub-Investigator: Evelien Verhaeghe, MD, PhD
      • Contact: Amber Shen, MHP; Phone Number: +3293322243; Email: amber.shen@uzgent.be
      • Contact: Julie Kips, MD; Phone Number: +32487519041; Email: julie.kips@ugent.be

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

Patients (> 18 years old) consulting at the Department of Dermatology of the Ghent University Hospital concerned about one or two skin lesions meeting at least one of the specified criteria.

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Only
• Time Perspectives: Cross-Sectional

What is the study measuring?

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

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patient characteristics related to the use of (medical) smartphone applications	Age, gender, education, use of a smartphone (yes or no), use of applications in general (e.g. social media, payment, music, reading or podcasts), and health-related or medical applications (qualitative measures: never/sometimes/often/all the time)	Day 1

Collaborators and Investigators

• Sponsor: University Hospital, Ghent
• Investigators: Principal Investigator: Lieve Brochez, MD, PhD, Ghent University Hospital, Department of Dermatology; Principal Investigator: Evelien Verhaeghe, MD, PhD, Ghent University Hospital, Department of Dermatology

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2020
• Primary Completion (Estimated): March 31, 2025
• Study Completion (Estimated): March 31, 2025

Study Registration Dates

• First Submitted: January 17, 2022
• First Submitted That Met QC Criteria: February 14, 2022
• First Posted (Actual): February 18, 2022

Study Record Updates

• Last Update Posted (Estimated): June 4, 2024
• Last Update Submitted That Met QC Criteria: June 3, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Keywords: Screening; Prevention; Artificial Intelligence; Skinvision App
• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Skin Neoplasms
• Other Study ID Numbers: 2017/0823 (BC-01191)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No