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

Condition(s) studied

Study 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

Primary outcome measure(s)

• Accuracy of artificial intelligence-based classification of basal cell carcinoma risk groups — Baseline
  Diagnostic accuracy of the convolutional neural network model in distinguishing low-risk and high-risk basal cell carcinoma using dermoscopic images, compared with histopathological diagnosis as the reference standard.

Trial sites (1)

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