Artificial Intelligence Based Melanoma Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults (AI-MEL)

September 27, 2024 updated by: German Cancer Research Center

AI-MEL: Image Analysis and Machine Learning for 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.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

3000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Germany
      • Tübingen, Germany, 72074
        • Completed
        • University of Tübingen
  • Italy
      • Florence, Italy, 50121
        • Completed
        • University of Florence
  • Spain
      • Barcelona, Spain, 08036
        • Recruiting
        • Hospital Clínic de Barcelona
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Mainly children (up to and including 15 years of age), adolescents (16-20) and young adults (from 21 to 30 years of age)

Description

Inclusion Criteria:

-

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
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.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
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.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

German Cancer Research Center

Collaborators

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

Investigators

  • Principal Investigator: Titus J Brinker, PD Dr. med, German Cancer Research Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 1, 2022

Primary Completion (Estimated)

November 30, 2026

Study Completion (Estimated)

November 30, 2026

Study Registration Dates

First Submitted

September 24, 2024

First Submitted That Met QC Criteria

September 27, 2024

First Posted (Actual)

October 1, 2024

Study Record Updates

Last Update Posted (Actual)

October 1, 2024

Last Update Submitted That Met QC Criteria

September 27, 2024

Last Verified

September 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • MELCAYA-AI-MEL

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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