Application of Artificial Intelligence-Based Ultrasound in the Differentiation of Benign and Malignant Breast Nodules
- VernacularTitle:Хөхний хоргүй болон хортой зангилааг хиймэл оюун ухаанд суурилсан хэт авиан шинжилгээгээр ялган оношилох асуудалд
- Author:
Lai-Fu Han
1
;
Shiirevnyamba A
2
;
Tsakhim-Erdene Ts
3
;
Bat-Amgalan B
3
;
Delgerdalai Kh
4
Author Information
1. Interdisciplinary Training Center, Graduate School, MNUMS
2. Department of Surgery, School of Medicine, MNUMS
3. Supply and Service Department, MNUMS
4. Department of Radiology, School of Medicine, MNUMS
- Publication Type:Journal Article
- Keywords:
Breast nodule;
Ultrasound imaging;
Artificial Intelligence (AI);
BI-RADS;
Diagnostic accuracy
- From:
Mongolian Journal of Health Sciences
2026;95(5):223-227
- CountryMongolia
- Language:Mongolian
-
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.
- Full text:2026100110585355558Хөхний хоргүй болон хортой зангилааг.pdf
