(untitled) (Vietnam Journal of Community Medicine, 2026)
Objective: To compare the diagnostic support value of dermoscopy and artificial intelligence (AI) using a convolutional neural network (StackNet model) in the diagnosis of basal cell carcinoma (BCC). Materials and Methods: A prospective cross-sectional descriptive study was conducted on 110 patients presenting with clinically suspected BCC lesions at the National Hospital of Dermatology and Venereology. Results: Dermoscopy demonstrated a sensitivity of 91.4% and a specificity of 73.7% for the diagnosis of BCC, improving diagnostic accuracy compared with clinical examination alone. Among 124 clinically suspected lesions, dermoscopy correctly identified 96 of 105 histopathologically confirmed BCC lesions. The AI model (StackNet) achieved a sensitivity of 94.3% and a specificity of 78.9%, correctly identifying 99 of 105 true BCC lesions, indicating superior diagnostic support compared with visual clinical assessment. The diagnostic performance of dermoscopy (AUROC = 0.826) and AI (AUROC = 0.866) was rated as good. The mean difference in AUROC between the two methods was 0.04; DeLong’s test showed no statistically significant difference between the methods (p > 0.05). Conclusion: Both dermoscopy and artificial intelligence significantly enhance the diagnostic accuracy of basal cell carcinoma compared with naked-eye clinical examination. The sensitivity and specificity of AI in diagnosing BCC were high. The diagnostic support value of dermoscopy and AI was comparable and rated as good, with no statistically significant difference between the two approaches.