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

Summary

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.

Eligibility

Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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.

Conditions2

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