The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis DOI Creative Commons
Sneha Singh, Nuala Healy

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Dec. 12, 2024

Abstract Introduction Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied real-world setting and multiple studies have conducted the area. The aim of this analysis to identify most influential publications on topic artificial imaging. Methods A retrospective bibliometric was using Web Science database. search strategy involved searching for keywords ‘breast radiology’ or imaging’ various associated with such as ‘deep learning’, ‘machine learning,’ ‘neural networks’. Results From top 100 list, number citations per article ranged from 30 346 (average 85). highest cited titled ‘Artificial Neural Networks Mammography—Application To Decision-Making Diagnosis Of Breast-Cancer’ published Radiology 1993. Eighty-three articles were last 10 years. journal greatest ( n = 22). common country origin United States 51). Commonly occurring topics use deep learning models cancer detection mammography ultrasound, radiomics cancer, risk prediction. Conclusion This study provides comprehensive most-cited papers subject discusses current Clinical relevance statement concise summary field radiology. It impactful explores recent trends research Key Points Multiple highlights Graphical

Language: Английский

AI in Breast Cancer Imaging: An Update and Future Trends DOI Creative Commons
Yizhou Chen, Xiaoliang Shao, Kuangyu Shi

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

3
New Frontiers in Breast Cancer Imaging: The Rise of AI DOI Creative Commons
Stephanie Shamir, Arielle Sasson, Laurie R. Margolies

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(5), P. 451 - 451

Published: May 2, 2024

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist the diagnosis and treatment patients. AI implementation radiology, more specifically for breast imaging, advanced considerably. Breast cancer is one most important causes mortality among women, there increased attention towards creating efficacious methods detection utilizing improve radiologist accuracy efficiency meet increasing demand our can be applied imaging studies image quality, increase interpretation accuracy, time cost efficiency. mammography, ultrasound, MRI allows improved while decreasing intra- interobserver variability. The synergistic effect between a potential patient care underserved populations with intention providing quality equitable all. Additionally, allowed risk stratification. Further, application have implications as well by identifying upstage ductal carcinoma situ (DCIS) invasive better predicting individualized response neoadjuvant chemotherapy. advancement pre-operative 3-dimensional models viability reconstructive grafts.

Language: Английский

Citations

10
SLC38A5 promotes glutamine metabolism and inhibits cisplatin chemosensitivity in breast cancer DOI
Xiaowei Shen, Ganggang Wang,

Hua He

et al.

Breast Cancer, Journal Year: 2023, Volume and Issue: 31(1), P. 96 - 104

Published: Nov. 2, 2023

Language: Английский

Citations

12
The Smart Performance Comparison of AI-based Breast Cancer Detection Models DOI
Sana Samreen, Abdul Sajid Mohammed,

Anuteja Reddy Neravetla

et al.

Published: Feb. 23, 2024

The smart performance comparison of AI-based breast cancer detection models is an important research topic in the healthcare industry. It used to compare and evaluate different that are diagnose cancer. These mainly developed using machine learning, computer vision, or deep learning techniques. methods these can vary depending on purpose comparison. This include comparing accuracy, precision, recall, f-measure models. Furthermore, other criteria such as stability, reliability, explain ability, speed, cost-effectiveness may be taken into consideration when evaluating have achieved high sensitivity specificity rates, outperforming traditional methods. However, AI varies based type imaging technique dataset used. Further, also highlights need for more diverse inclusive datasets avoid potential biases results from this provide valuable insight help professionals researchers select deploy best model their particular applications.

Language: Английский

Citations

4
Assessment of breast composition in MRI using artificial intelligence – A systematic review DOI Creative Commons

Philip Murphy,

Mark F. McEntee, Michael M. Maher

et al.

Radiography, Journal Year: 2025, Volume and Issue: 31(2), P. 102900 - 102900

Published: Feb. 20, 2025

Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family history. MRI could take advantage of the implementation artificial intelligence (AI). AI assessment composition factors is less studied than those lesion detection and classification. These are density, background parenchymal enhancement (BPE) fibroglandular tissue (FGT), which recognized phenotypes. Following PRISMA guidelines, PROSPERO registered review examined assessing MRI. A search articles from Pubmed, Ovid, Embase, Web Science, Cochrane, Google scholar 2010 to 2022 was conducted. Peer-reviewed, in-vivo studies were included based on defined categories. Adapted QUADAS-2, CASP Covidence tools utilized quality assessment. Seven identified as being sufficiently high quality. The showed that has potential provide comparable level accuracy against relevant reference standard. There limited performance results when delineating BPE FGT BI-RADs highlighted variability models while range statistical methods small cohort sizes cross study compatibility. However, systems deployed measurements alongside validation across diverse remain an issue. may perform better with binary categorizations rather quaternary spectrum BI-RADS. assist developing personalized assessments. Future developments focus delineation have trained more larger should result robust effective clinical applications.

Language: Английский

Citations

0
Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights DOI Creative Commons
Xinyu Wu,

Yufei Xia,

Xinjing Lou

et al.

Breast Cancer Research, Journal Year: 2025, Volume and Issue: 27(1)

Published: Feb. 25, 2025

Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct bibliometrics field discuss its research status frontier hotspots provide reference for subsequent research. Publications related AI, radiomics, imaging were searched the Web of Science Core Collection. CiteSpace plotted relevant co-occurrence network according authors keywords. VOSviewer Pajek draw maps country institution. In addition, R was bibliometric authors, countries/regions, journals, keywords, annual publications citations based on collected information. A total 2,701 Collection retrieved, including 2,486 articles (92.04%) 215 reviews (7.96%). The number increased rapidly after 2018. United States America (n = 17,762) leads citations, while China 902) publications. Sun Yat-sen University 75) had largest Bin Zheng 28) most published author. Nico Karssemeijer 72.1429) author with highest average citations. "Frontiers Oncology" journal publications, "Radiology" IF. keywords frequent occurrence "breast cancer", "deep learning", "classification". topic trends recent years "explainable AI", "neoadjuvant chemotherapy", "lymphovascular invasion". application radiomics has received extensive attention. Future may mainly focus progress explainable technical prediction lymphovascular invasion neoadjuvant chemotherapy efficacy clinical application.

Language: Английский

Citations

0
Evaluating the Effectiveness of Explainable AI Techniques in Interpreting Breast Cancer Diagnoses Across Multiple Imaging Modalities DOI

禄 李

Advances in Clinical Medicine, Journal Year: 2025, Volume and Issue: 15(02), P. 1503 - 1512

Published: Jan. 1, 2025

Language: Английский

Citations

0
Benchmarking Deep Learning Algorithms for Breast Cancer Detection: A Comprehensive Review and Evaluation Across Public Imaging Datasets DOI Creative Commons
Dariush Moslemi, Seyed Mohammad Hassan Hosseini,

Elham Jafarian

et al.

InfoScience Trends, Journal Year: 2025, Volume and Issue: 2(4), P. 11 - 24

Published: April 14, 2025

Language: Английский

Citations

0
Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software DOI Creative Commons

Anna Maria Pavone,

Antonino Giulio Giannone, Daniela Cabibi

et al.

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(1), P. 173 - 196

Published: Jan. 11, 2024

In the era of digitalization, biomedical sector has been affected by spread artificial intelligence. recent years, possibility using deep and machine learning methods for clinical diagnostic therapeutic interventions emerging as an essential resource imaging. Digital pathology represents innovation in a world that looks faster better-performing methods, without losing accuracy current human-guided analyses. Indeed, intelligence played key role wide variety applications require analysis massive amount data, including segmentation processes medical this context, enables improvement image moving towards development fully automated systems able to support pathologists decision-making procedures. The aim review is aid biologists clinicians discovering most common open-source tools, ImageJ (v. 1.54), CellProfiler 4.2.5), Ilastik 1.3.3) QuPath 0.4.3), along with their customized implementations. Additionally, tools’ histological imaging field explored further, suggesting potential application workflows. conclusion, encompasses examination commonly segmented tissues through tools.

Language: Английский

Citations

3
The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis DOI Creative Commons
Sneha Singh, Nuala Healy

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Dec. 12, 2024

Abstract Introduction Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied real-world setting and multiple studies have conducted the area. The aim of this analysis to identify most influential publications on topic artificial imaging. Methods A retrospective bibliometric was using Web Science database. search strategy involved searching for keywords ‘breast radiology’ or imaging’ various associated with such as ‘deep learning’, ‘machine learning,’ ‘neural networks’. Results From top 100 list, number citations per article ranged from 30 346 (average 85). highest cited titled ‘Artificial Neural Networks Mammography—Application To Decision-Making Diagnosis Of Breast-Cancer’ published Radiology 1993. Eighty-three articles were last 10 years. journal greatest ( n = 22). common country origin United States 51). Commonly occurring topics use deep learning models cancer detection mammography ultrasound, radiomics cancer, risk prediction. Conclusion This study provides comprehensive most-cited papers subject discusses current Clinical relevance statement concise summary field radiology. It impactful explores recent trends research Key Points Multiple highlights Graphical

Language: Английский

Citations

0