Acne Lesions, Acne Vulgaris
Conditions
Keywords
machine learning, self-learning software, image-detecting software
Brief summary
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.
Interventions
OTHERSelf- learning software that can detect acne lesions
Self- learning software that can detect acne lesions from patients who take a picture of their face every single day for 3 months with a secure mobile phone.
OTHERPatient reported outcomes
Collection of patient reported outcomes and clinical data via a mobile electronic case report form
Sponsors
University Hospital, Basel, Switzerland
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
10 Years to 35 Years
Healthy volunteers
No
Inclusion criteria
* Acne vulgaris
Exclusion criteria
* Refusal to participate
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pictures to train the AcneDect software | every single day from baseline for 3 months | Collection of pictures to train the AcneDect software to detect change in acne lesions |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| AcneDect questionnaire regarding acne burden (Visual Analogue Scale (VAS) scale ranging from Not bad at all to Very bad) | every single day from baseline for 3 months | Collection of patient reported outcomes via a mobile electronic case report form |
Countries
Switzerland
Outcome results
None listed