Clinical trial · Observational
Artificial Intelligence Based Melanoma Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this study is to develop supportive diagnostic artificial intelligence algorithms to distinguish melanoma from nevi or other benign pigmented skin lesions, especially in younger patients (below the age of 30). The main goals it aims to achieve are: * development of an algorithm based on dermatoscopic images, targeting skin cancer screening in vulnerable populations * development of another algorithm based on histological images, intended to be used by pathologists on lesions that are still suspicious of melanoma after dermatologic assessment * implementation of explainability methods to enable the user to better comprehend the systems' decisions, avoid biases and increase trust in these applications There is no additional time commitment for the study participants for this study, as the data used in this project will be collected in routine clinical practice anyway.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence (AI) | — | UNRESOLVED | — |
| Melanoma (Skin Cancer) | Melanoma | ONTOLOGY_EXACT | 0.85 |
| Pediatric Cancer | Childhood Malignant Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Area Under the Receiver Operator Curve (AUROC)
- timeFrame
- First Assessment: Upon completion of the first training and testing cycle (approx. within 1.5 years from the start of the study). Reevaluations: at 6 and 12 months post-initial training for model improvement.
- description
- The AUROC is used to measure and compare the diagnostic accuracy of different classifiers. Thereby, a higher value means better diagnostic performance, with an AUROC of 1 being a perfect score.
Secondary outcomes (1)
- measure
- Balanced accuracy
- timeFrame
- First Assessment: Upon completion of the first training and testing cycle (approx. within 1.5 years from the start of the study). Reevaluations: at 6 and 12 months post-initial training for model improvement.
- description
- The balanced accuracy is used to measure and compare the diagnostic accuracy between classifier and physician. Thereby, a higher value means better diagnostic performance, with a balanced accuracy of 1 signifying perfect diagnostic capabilities.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: \- Exclusion Criteria: * Patients without a melanoma or nevus diagnosis * images with insufficient image quality
References
Publications (0)
Data not yet available