AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Melanoma (Skin Cancer)
- Sponsor
- German Cancer Research Center
- Enrollment
- 3000
- Locations
- 3
- Primary Endpoint
- Area Under the Receiver Operator Curve (AUROC)
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
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.
Investigators
Eligibility Criteria
Inclusion Criteria
- Not provided
Exclusion Criteria
- •Patients without a melanoma or nevus diagnosis
- •images with insufficient image quality
Outcomes
Primary Outcomes
Area Under the Receiver Operator Curve (AUROC)
Time Frame: 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.
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
- Balanced accuracy(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.)