Training and validation of an automated diagnostic deep learning algorithm in dermoscopy

Recruiting

• Conditions: C43.9; D22.9; Malignant melanoma of skin, unspecified; Melanocytic naevi, unspecified

• Registration Number: DRKS00013570
• Lead Sponsor: niversitätsklinikum Heidelberg

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Study Start: 2017-12-14
• Last Update Posted: 19 August 2024

Recruitment & Eligibility

• Status: Recruiting
• Sex: All
• Target Recruitment: 300

Inclusion Criteria

Patients with dermoscopic images depicting nevi or melanoma

Exclusion Criteria

Low Image quality

Study & Design

• Study Type: observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Objective of this retrospective analysis is the assessment of the diagnostic performance of a computer algorithm in analyzing dermoscopic images in comparison to dermatologists. Diagnostic Performance will be assessed by means of sensitivity and specificity. Estimated study Duration is 12 months.

Secondary Outcome Measures

Name	Time	Method
There are no secondary objectives in this study