Application of Artificial Intelligence-Based Ultrasound in the Differentiation of Benign and Malignant Breast Nodules

Abstract

Background: Early and accurate differentiation of benign and malignant breast nodules is crucial for determining appropriate therapeutic strategies. Recently, artificial intelligence (AI)-based diagnostic systems have been increasingly utilized to enhance the diagnostic performance of ultrasound imaging. Aim: To evaluate the diagnostic value of artificial intelligence-based ultrasound (AI-US) in differentiating benign and malignant breast nodules and to compare its performance with conventional ultrasound (US). Materials and Methods: This retrospective study included 101 patients with 103 breast nodules (58 malignant, 45 benign) confirmed by histopathological analysis (biopsy or surgery). Diagnostic performance was evaluated across three groups: 1) Conventional ultrasound (US), 2) AI system (AI group), and 3) Combined US and AI system (US+AI group). Sensitivity (SE), specificity (SP), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve (AUC) were calculated and compared. Results: Of the 103 nodules, 56.3% were malignant and 43.7% were benign. The AI group demonstrated higher diagnostic metrics than the US group—SE (82.76%), SP (80.00%), ACC (81.55%), and AUC (0.814)—though the difference was not statistically significant (p>0.05). However, the combined US+AI group showed significant improvement, reaching an SE of 87.93%, SP of 86.67%, ACC of 87.38%, and an AUC of 0.874. These results were statistically superior to the conventional US group (p<0.05). Conclusion: The AI-based ultrasound diagnostic system significantly improves the accuracy of differentiating breast nodules. This technology serves as a reliable decision-support tool, particularly for reducing diagnostic errors among less experienced physicians and minimizing unnecessary invasive biopsies.