The role of Artificial Intelligence in detecting breast lesions using ultrasound
DOI:
https://doi.org/10.12775/QS.2025.37.57301Keywords
Artificial intelligence, breast imaging, computer-aided detection, breast cancer, BI-RADS scale, image analysisAbstract
Introduction and objective: Breast cancer is the most diagnosed cancer and the second leading cause of cancer deaths in women globally, with rising cases and mortality. Early detection via mammography, ultrasound, or MRI is vital, with ultrasound excelling in dense breast tissue due to its safety and accuracy.
Review methods: A literature review utilizing databases like Scopus, Google Scholar, and PubMed, with keywords such as "AI use in radiology" and "BI-RADS scale" underscores the need for advancements in understanding and managing graft rejection.
Brief knowledge status: AI develops systems that simulate human intelligence, excelling in breast imaging by detecting patterns and providing accurate results. Machine learning (ML) and deep learning (DL) drive advances, with DL's CNNs leading in image analysis. AI aids BI-RADS lesion classification, ultrasound lesion detection, lymph node analysis, and treatment response prediction, often surpassing radiologists. Its future relies on real-world validation, improved outcomes, and clinical integration.
Discussion: The integration of artificial intelligence (AI) into breast imaging marks a transformative leap in diagnostic radiology, enhancing precision, efficiency, and scalability. Driven by advancements in machine learning (ML) and deep learning (DL), AI excels in analyzing complex datasets. However, its clinical adoption requires addressing key considerations with a nuanced approach.
Summary: In conclusion, AI holds immense promise in breast imaging, poised to redefine the field through enhanced diagnostic capabilities and clinical utility. Continued advancements and validation efforts will ensure its broader acceptance and sustained impact in medical imaging.
References
1. Giaquinto, A. N., Sung, H., Newman, L. A., Freedman, R. A., Smith, R. A., Star, J., Jemal, A., & Siegel, R. L. (2024). Breast cancer statistics 2024. CA: a cancer journal for clinicians, 74(6), 477–495. https://doi.org/10.3322/caac.21863
2. Berg, W. A., Blume, J. D., Cormack, J. B., Mendelson, E. B., Lehrer, D., Böhm-Vélez, M., Pisano, E. D., Jong, R. A., Evans, W. P., Morton, M. J., Mahoney, M. C., Larsen, L. H., Barr, R. G., Farria, D. M., Marques, H. S., Boparai, K., & ACRIN 6666 Investigators (2008). Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA, 299(18), 2151–2163. https://doi.org/10.1001/jama.299.18.2151
3. Hamid NS, Portnoy JM, Pandya A. Computer-Assisted Clinical Diagnosis and Treatment. 2023 Jun 23;
4. Zheng, D., He, X., & Jing, J. (2023). Overview of Artificial Intelligence in Breast Cancer Medical Imaging. Journal of clinical medicine, 12(2), 419. https://doi.org/10.3390/jcm12020419
5. Kwon, H., Oh, S. H., Kim, M. -G., Kim, Y., Jung, G., Lee, H. -J., Kim, S. -Y., & Bae, H. -M. (2024). Enhancing Breast Cancer Detection through Advanced AI-Driven Ultrasound Technology: A Comprehensive Evaluation of Vis-BUS. Diagnostics, 14(17), 1867. https://doi.org/10.3390/diagnostics14171867
6. Songsaeng, C., Woodtichartpreecha, P., & Chaichulee, S. (2021). Multi-scale convolutional neural networks for classification of digital mammograms with breast calcifications. IEEE Access, 9, 114741-114753.
7. Taghipour zahir, S., Aminpour, S., Jafari-Nedooshan J., et al. (2023). Comparative study of breast core needle biopsy (CNB) findings with ultrasound BI-RADS subtyping. Pol Przegl Chir, 95(2), 11-17. https://doi.org/10.5604/01.3001.0015.8480.
8. Bednarski, P., Dobruch-Sobczak, K., Chrapowicki, E., & Jakubowski, W. (2015). Badanie ultrasonograficzne piersi–oczekiwania chirurga. Journal of Ultrasonography, 15(61), 164-171.
9. Nahar, Z., Kabir, M. E., Alam, T., Yasmin, S., & Naowar, M. (2017). Comparison of Mammography and Ultrasonography in Evaluation of Breast Masses. Journal of Armed Forces Medical College, Bangladesh, 13(2), 22-24.
10. Chakkarapani, R., & Durairaj, B. (2018). Diagnostic accuracy of mammography and ultrasonography for breast diseases. Journal of evolution of medical and dental sciencesjemds, 7, 592-596.
11. Badu-Peprah, A., & Adu-Sarkodie, Y. (2018). Accuracy of clinical diagnosis, mammography and ultrasonography in preoperative assessment of breast cancer. Ghana medical journal, 52(3), 133-139.
12. Shin, H. J., Kim, H. H., & Cha, J. H. (2015). Current status of automated breast ultrasonography. Ultrasonography, 34(3), 165.
13. Pereira, R. D. O., Luz, L. A. D., Chagas, D. C., Amorim, J. R., Nery-Júnior, E. D. J., Alves, A. C. B. R., ... & Silva, B. B. D. (2020). Evaluation of the accuracy of mammography, ultrasound and magnetic resonance imaging in suspect breast lesions. Clinics, 75, e1805.
14. Ibrahim A, Gamble P, Jaroensri R et al. Artificial intelligence in digital breast pathology: Techniques and applications. Breast. 2020 Feb;49:267-273. https://doi.org/10.1016/j.breast.2019.12.007
15. Ramirez Zegarra R, Ghi T. Use of artificial intelligence and deep learning in fetal ultrasound imaging. Ultrasound Obstet Gynecol. 2023 Aug;62(2):185-194. https://doi.org/10.1002/uog.26130
16. Bahl M. Updates in Artificial Intelligence for Breast Imaging. Semin Roentgenol. 2022 Apr;57(2):160-167. https://doi.org/10.1053/j.ro.2021.12.005
17. Balkenende L, Teuwen J, Mann RM. Application of Deep Learning in Breast Cancer Imaging. Semin Nucl Med. 2022 Sep;52(5):584-596. https://doi.org/10.1053/j.semnuclmed.2022.02.003
18. Thomassin-Naggara I, Balleyguier C, Ceugnart L et al. Artificial intelligence and breast screening: French Radiology Community position paper. Diagn Interv Imaging. 2019 Oct;100(10):553-566. https://doi.org/10.1016/j.diii.2019.08.005
19. Singh VK, Abdel-Nasser M, Akram F, Rashwan HA, Sarker MdMK, Pandey N, et al. Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. Expert Systems with Applications. 2020 Dec;162:113870.
20. Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. European Radiology. 2023 Sep 2;34.
21. P. B. Deshmukh and K. L. Kashyap, "Research Challenges in Breast Cancer Classification through Medical Imaging Modalities using Machine Learning," 2021 International Conference on Industrial Electronics Research and Applications (ICIERA), New Delhi, India, 2021, pp. 1-5, doi: 10.1109/ICIERA53202.2021.9726746.
22. Bai J, Posner R, Wang T, Yang C, Nabavi S. Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review. Medical Image Analysis. 2021 Jul;71:102049.
23. Vobugari, N., Raja, V., Sethi, U., Gandhi, K., Raja, K., & Surani, S. R. (2022). Advancements in Oncology with Artificial Intelligence—A Review Article. Cancers, 14(5), 1349. https://doi.org/10.3390/cancers14051349
24. Komatsu, M., Sakai, A., Dozen, A., Shozu, K., Yasutomi, S., Machino, H., Asada, K., Kaneko, S., & Hamamoto, R. (2021). Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines, 9(7), 720. https://doi.org/10.3390/biomedicines9070720
25. Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, et al. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Frontiers in Oncology [Internet]. 2022;12:773840.
26. Tadavarthi Y, Vey B, Krupinski E, Prater A, Gichoya J, Safdar N, et al. The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings. Radiology: Artificial Intelligence. 2020 Nov 1;2(6):e200004.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Daria Ziemińska, Karina Motolko, Rafał Burczyk, Konrad Duszyński, Elżbieta Tokarczyk, Martyna Michalska, Adam Łabuda
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Stats
Number of views and downloads: 158
Number of citations: 0
