AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 3,000
- 试验地点
- 3
- 主要终点
- Area Under the Receiver Operator Curve (AUROC)
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Other
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- 未提供
排除标准
- •Patients without a melanoma or nevus diagnosis
- •images with insufficient image quality
结局指标
主要结局
Area Under the Receiver Operator Curve (AUROC)
时间窗: 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.
次要结局
- 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.)