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.
None
Patients without a melanoma or nevus diagnosis
images with insufficient image quality
Mainly children (up to and including 15 years of age), adolescents (16-20) and young adults (from 21 to 30 years of age)
Case-only
Other
Not listed
Area Under the Receiver Operator Curve (AUROC)
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.
Balanced accuracy
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.
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German Cancer Research Center
Lead sponsor
Universität Tübingen
Collaborator
University of Florence
Collaborator
Fundacio Clinic Barcelona
Collaborator
Hospital Clinic of Barcelona
Collaborator
