AI-Based Risk Classification and Histopathological Subtype Prediction of Basal Cell Carcinoma Using Dermoscopic Images

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Conditions studied: Basal Cell Carcinoma

In brief

This retrospective observational study aims to develop and evaluate a convolutional neural network (CNN)-based artificial intelligence model for risk classification and histopathological subtype prediction of basal cell carcinoma (BCC) using clinical and dermoscopic images. Histopathologically confirmed BCC cases from a dermatology archive will be included. The primary objective is to assess the diagnostic performance of the CNN model in classifying BCC as low-risk or high-risk. Secondary objectives include predicting histopathological subtypes and comparing the model's performance with that of dermatology physicians. Histopathological diagnosis will serve as the reference standard. All archived data will be anonymized before analysis.

Key facts

Study ID: NCT07677124
Run by: Istanbul Training and Research Hospital
People needed: 2500
Starts: 2026-05-22
Expected to finish: 2027-05-22
Last updated by the study team: 2026-06-30

Who can join

Age: any, up to 100. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

Patients with histopathologically confirmed basal cell carcinoma.
Cases with a specified histopathological subtype.
Availability of dermoscopic images with sufficient image quality and resolution for artificial intelligence analysis.

You may not qualify if…

Cases without histopathological confirmation of basal cell carcinoma.
Cases with unspecified histopathological subtype.
Images with insufficient quality or resolution for artificial intelligence analysis.
Cases without available dermoscopic images.

Where it is running

Istanbul Training and Research Hospital — Istanbul, Istanbul, Turkey (Türkiye) (enrolling)

Full record on ClinicalTrials.gov

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