Acne Detection Software (AcneDect) (AcneDect)

Acne Detection Software (AcneDect); AcneDect: a Software to Detect Acne Lesions

This study is to create a self-learning software that can detect acne lesions. Patients take a picture of their face every single day for 3 months with a secure mobile phone and fill out a pre-designed questionnaire. After 3 months, the mobile will be collected back and the pictures will be evaluated by 3 dermatologists. The software is able to learn from the dermatologists' evaluation and -using machine learning- a mechanism that should be able to automatically detect acne to some extent will be established.

Study Overview

• Status: Terminated
• Conditions: Acne Vulgaris; Acne Lesions
• Intervention / Treatment: Other: Self- learning software that can detect acne lesions; Other: Patient reported outcomes

• Study Type: Observational
• Enrollment (Actual): 25

Contacts and Locations

Study Locations

• Switzerland
  • Basel, Switzerland, 4031
    • Department of Dermatology, University Hospital Basel

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 10 years to 35 years (Child, Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients within the consultation service of Dermatologische Klinik, Universitätsspital Basel, that meet the inclusion criteria

Description

Inclusion Criteria:

• Acne vulgaris

Exclusion Criteria:

• Refusal to participate

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Pictures to train the AcneDect software	Collection of pictures to train the AcneDect software to detect change in acne lesions	every single day from baseline for 3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AcneDect questionnaire regarding acne burden (Visual Analogue Scale (VAS) scale ranging from "Not bad at all" to "Very bad")	Collection of patient reported outcomes via a mobile electronic case report form	every single day from baseline for 3 months

Collaborators and Investigators

• Sponsor: University Hospital, Basel, Switzerland
• Investigators: Principal Investigator: Alexander A. Navarini, Prof. Dr. MD, Dermatologische Klinik; Universitätshospital Basel

Study record dates

Study Major Dates

• Study Start (Actual): October 29, 2020
• Primary Completion (Actual): September 30, 2024
• Study Completion (Actual): September 30, 2024

Study Registration Dates

• First Submitted: August 15, 2019
• First Submitted That Met QC Criteria: August 15, 2019
• First Posted (Actual): August 16, 2019

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 13, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Keywords: machine learning; self-learning software; image-detecting software
• Additional Relevant MeSH Terms: Skin Diseases; Acneiform Eruptions; Sebaceous Gland Diseases; Acne Vulgaris
• Other Study ID Numbers: 2018-00702; sp19Navarini

Drug and device information, study documents

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