Retrospective study on the technical feasibility of analysis algorithms for the detection of dermatological and radiological diseases at a medical level using image data

Retrospective study on the technical feasibility of analysis algorithms for the detection of dermatological and radiological diseases at a medical level using image data - DR-AI

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00024650

Enrollment

900000

Registered

2023-02-15

Start date

2021-05-17

Completion date

Unknown

Last updated

2025-10-06

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

Conditions

L98.9

Interventions

Group 1: It is a retrospective study design in which all the data to be analyzed are already available. The corresponding image files were acquired at the LMU Clinic. There is no additional burden for

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Use only images with a registered dermatological diagnosis Cross-validation with the histological diagnosis

Exclusion criteria

Exclusion criteria: Insufficient image quality Pronounced image artifacts Complete anonymization not possible

Design outcomes

Primary

Measure	Time frame
The aim of the present study is to demonstrate the technical feasibility of analysis algorithms for the detection of dermatological and radiological diseases at the medical level using image data through programming, training and validation using retrospective image data.	—

Secondary

Measure	Time frame
The results of the algorithms are compared with the reference standards specified by doctors. Key statistical parameters include: accuracy, sensitivity, specificity, F1 score and confusion matrix.	—

Countries

Germany

Contacts

Public ContactBastian Sabel

LMU München

Bastian.Sabel@med.uni-muenchen.de+4989440076642

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026