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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06621810

Acronym

AI-MEL

Enrollment

3000

Registered

2024-10-01

Start date

2022-12-01

Completion date

2026-11-30

Last updated

2024-10-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence (AI), Melanoma (Skin Cancer), Pediatric Cancer

Keywords

melanoma, AI, pediatric, image analysis, Children Adolescence Young Adults (CAYA)

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.

Interventions

None listed

Study design

Observational model

CASE_ONLY

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Healthy volunteers

Yes

Inclusion criteria

Exclusion criteria

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

Design outcomes

Primary

Measure	Time frame	Description
Area Under the Receiver Operator Curve (AUROC)	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

Measure	Time frame	Description
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.	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.

Countries

Germany, Italy, Spain

Contacts

Primary ContactTitus J Brinker, PD Dr. med

titus.brinker@nct-heidelberg.de+49 15175084347

Outcome results