The role of Artificial Intelligence in detecting breast lesions using ultrasound
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 a...
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| Format: | Article |
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Nicolaus Copernicus University in Toruń
2025-01-01
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| Series: | Quality in Sport |
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| Online Access: | https://apcz.umk.pl/QS/article/view/57301 |
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| author | Daria Ziemińska Karina Motolko Rafał Burczyk Konrad Duszyński Elżbieta Tokarczyk Martyna Michalska Adam Łabuda |
| author_facet | Daria Ziemińska Karina Motolko Rafał Burczyk Konrad Duszyński Elżbieta Tokarczyk Martyna Michalska Adam Łabuda |
| author_sort | Daria Ziemińska |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-351eb9b4a780438d91f65f2c23a1d6cf |
| institution | Kabale University |
| issn | 2450-3118 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nicolaus Copernicus University in Toruń |
| record_format | Article |
| series | Quality in Sport |
| spelling | doaj-art-351eb9b4a780438d91f65f2c23a1d6cf2025-01-15T08:23:08ZengNicolaus Copernicus University in ToruńQuality in Sport2450-31182025-01-013710.12775/QS.2025.37.57301The role of Artificial Intelligence in detecting breast lesions using ultrasoundDaria Ziemińska0https://orcid.org/0009-0001-8240-2593Karina Motolko1https://orcid.org/0009-0001-9971-6687Rafał Burczyk2https://orcid.org/0000-0002-1650-1534Konrad Duszyński3https://orcid.org/0009-0006-5524-8857Elżbieta Tokarczyk4https://orcid.org/0009-0003-9683-7699Martyna Michalska5https://orcid.org/0009-0002-3467-4364Adam Łabuda6https://orcid.org/0009-0005-1978-644XSzpital św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 GdyniaSpecjalistyczny Szpital Miejski im. M. Kopernika w Toruniu, ul. Stefana Batorego 17/19, 87-100 ToruńSzpital Uniwersytecki nr 2 im. dr J. Biziela, ul. Ujejskiego 75, 85-168 BydgoszczStudenckie Koło Naukowe Okulistyki, Gdański Uniwersytet Medyczny, ul. Marii Skłodowskiej-Curie 3a, 80-210 GdańskSzpital św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 GdyniaSzpital św. Wincentego a Paulo w Gdyni, ul. Wójta Radtkego 1, 81-348 Gdynia5 Wojskowy Szpital Kliniczny z Polikliniką SPZOZ, ul. Wrocławska 1 /3, 30-901 Kraków 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. https://apcz.umk.pl/QS/article/view/57301Artificial intelligencebreast imagingcomputer-aided detectionbreast cancerBI-RADS scaleimage analysis |
| spellingShingle | Daria Ziemińska Karina Motolko Rafał Burczyk Konrad Duszyński Elżbieta Tokarczyk Martyna Michalska Adam Łabuda The role of Artificial Intelligence in detecting breast lesions using ultrasound Quality in Sport Artificial intelligence breast imaging computer-aided detection breast cancer BI-RADS scale image analysis |
| title | The role of Artificial Intelligence in detecting breast lesions using ultrasound |
| title_full | The role of Artificial Intelligence in detecting breast lesions using ultrasound |
| title_fullStr | The role of Artificial Intelligence in detecting breast lesions using ultrasound |
| title_full_unstemmed | The role of Artificial Intelligence in detecting breast lesions using ultrasound |
| title_short | The role of Artificial Intelligence in detecting breast lesions using ultrasound |
| title_sort | role of artificial intelligence in detecting breast lesions using ultrasound |
| topic | Artificial intelligence breast imaging computer-aided detection breast cancer BI-RADS scale image analysis |
| url | https://apcz.umk.pl/QS/article/view/57301 |
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