Artificial Intelligence Based Melanoma Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults

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.

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Key information

Conditions

Melanoma (Skin Cancer) Artificial Intelligence (AI) Melanoma Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Germ Cell and Embryonal Neoplasms, Nerve Tissue Neuroectodermal Tumors Neuroendocrine Tumors Nevi and Melanomas Pediatric Cancer Skin Diseases Skin Neoplasms Skin and Connective Tissue Diseases

Sex eligibility

All sexes

Study type

Observational

Primary location

University of Tübingen, Tübingen, Germany

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Exclusion criteria

Patients without a melanoma or nevus diagnosis
images with insufficient image quality

Treatment and study plan

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

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 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Titus J Brinker, PD Dr. med

CONTACT

[email protected]

+49 15175084347

Sponsors and collaborators

Lead sponsor

German Cancer Research Center

Other

Collaborators

Fundacio Clinic Barcelona
Hospital Clinic of Barcelona
University of Florence
Universität Tübingen

Registry information

Official study title

AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults

Acronym: AI-MEL

Important dates

Study start: 2022
Primary completion: 2026
Study completion: 2026
First posted: Oct 1, 2024
Registry last updated: Oct 1, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Recruiting