Using AI to diagnose melanoma in young patients
Official title: AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
Run by German Cancer Research Center · NCT06621810 · Data updated 2026-11-12
This study is testing if artificial intelligence can help doctors better identify melanoma in children and young adults by looking at skin images.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 3000 (estimated) |
| Sponsor | German Cancer Research Center Academic / other |
| Locations | 3 sites (Tübingen and 2 other locations) |
| Status | Recruiting |
| Started | December 1, 2022 |
| Main results expected | November 30, 2026 |
| Study ends | November 30, 2026 |
| Registry record updated | October 1, 2024 |
| Trial ID | NCT06621810 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are children, adolescents, and young adults under the age of 30 who are undergoing routine skin assessments.
✕Probably not a fit
Patients without a melanoma or nevus diagnosis or those with insufficient image quality will not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study aims to develop artificial intelligence algorithms to help distinguish melanoma from benign skin lesions in children, adolescents, and young adults. It focuses on creating algorithms based on dermatoscopic and histological images to enhance skin cancer screening and assist pathologists. The study will utilize data collected during routine clinical practice, requiring no additional time commitment from participants. Explainability methods will also be implemented to improve user understanding and trust in the AI systems.
Why it matters
Potential benefit: If successful, this study could lead to earlier and more accurate diagnosis of melanoma in younger patients, potentially improving outcomes.
How similar studies have gone: Other studies have shown promise in using AI for melanoma detection, indicating that this approach has potential for success.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: \- Exclusion Criteria: * Patients without a melanoma or nevus diagnosis * images with insufficient image quality
Where it's running
-
Hospital Clínic de Barcelona
Barcelona, Spain Recruiting -
University of Tübingen
Tübingen, Germany Completed -
University of Florence
Florence, Italy Completed
Study contacts
- Principal investigator: Titus J Brinker, PD Dr. med — German Cancer Research Center
- Study coordinator: Titus J Brinker, PD Dr. med
- Email: titus.brinker@nct-heidelberg.de
- Phone: +49 15175084347
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
FaceSay: a computer program to help kids recognize facial expressions and improve social interactions
A computer-based FaceSay program is tested to see if it helps survivors of pediatric brain tumors who have social difficulties see facial expressions better and relate more comfortably with peers.
-
AI-assisted whole-course management for safe medicine use in advanced solid tumors
A real-world study compares a smartphone-based digital management system with usual care to see if it reduces drug-related problems and healthcare needs for adults with advanced solid tumors receiving cancer therapy.
-
PD-L1-targeted PET probe to map PD-L1 in solid tumors
A PET tracer called 18F-F-PDL1-3 is being tested to see if it can detect PD-L1 expression in primary and metastatic tumors to help guide personalized treatment.
-
ctDNA-guided stopping of immune therapy in advanced melanoma
This study tests whether stopping immune checkpoint inhibitor therapy after 12 months is safe for patients with advanced melanoma who have undetectable ctDNA and disease control.
-
ENPP1/cGAS and FAP in Solid Tumors in Vietnamese Patients
This Vietnam-based study will test ENPP1, cGAS, and FAP expression in archived tumor samples from Vietnamese adults with solid cancers using immunohistochemistry.
-
AI-assisted and traditional workflows for admission diagnosis and management planning
This trial tests whether an AI-agent workflow helps doctors make better admission diagnoses and care plans on simulated cases, compared with plain AI-assisted and traditional workflows.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.