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On Fri, 23 Aug, 12:04 AM UTC
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Novel AI-based algorithm could improve mammogram density assessment
University of Eastern Finland (UEF Viestintä)Aug 22 2024 Researchers at the University of Eastern Finland have developed a novel artificial intelligence-based algorithm, MV-DEFEAT, to improve mammogram density assessment. This development holds promise for transforming radiological practices by enabling more precise diagnoses. High breast tissue density is associated with an increased risk of breast cancer, and breast tissue density can be estimated from mammograms. The accurate assessment of mammograms is crucial for effective breast cancer screening, yet challenges such as variability in radiological evaluations and a global shortage of radiologists complicate these efforts. The MV-DEFEAT algorithm aims to address these issues by incorporating deep learning techniques that evaluate multiple mammogram views at the same time for mammogram density assessment, mirroring the decision-making process of radiologists. The research team involved with AI in cancer research consists of Doctoral Researcher Gudhe Raju, Professor Arto Mannermaa and Senior Researcher Hamid Behravan. In the present study, they employed an innovative multi-view deep evidential fusion approach. Their method leverages elements of the Dempster-Shafer evidential theory and subjective logic to assess mammogram images from multiple views, thus providing a more comprehensive analysis. MV-DEFEAT showed remarkable improvements over existing approaches. It demonstrates a significant improvement in mammogram screening accuracy by automatically and reliably quantifying the density and distribution of dense breast tissue within mammograms. For instance, in the public VinDr-Mammo dataset which consists of over 10,000 mammograms, the algorithm has achieved an impressive 50.78% improvement in distinguishing between benign and malignant tumours over the existing multi-view approach. Interestingly, the algorithm's effectiveness persisted across different datasets, reflecting its robust performance to adapt to diverse patient demographics. The study utilised extensive data from four open-source datasets, enhancing the algorithm's applicability and accuracy across different populations. Such capabilities underline the importance of AI-based approaches in medical diagnostics. Furthermore, while MV-DEFEAT significantly aids in breast cancer screening, the team at the University of Eastern Finland emphasises the need for continued refinement and validation of the algorithm to ensure its reliability and efficacy in clinical settings. These promising results pave the way for the use of AI in enhancing diagnostic processes, potentially leading to earlier detection and better patient outcomes in breast cancer care. To fully integrate AI like MV-DEFEAT into clinical practice, it is crucial to build trust among healthcare professionals through rigorous testing and validation. Indeed, our next steps involve further validation studies to establish MV-DEFEAT as a reliable tool for breast cancer diagnostics in Finland." Raju Gudhe, Doctoral Researcher of the University of Eastern Finland University of Eastern Finland (UEF Viestintä) Journal reference: Gudhe, N.R., et al. (2024). A Multi-view deep evidential learning approach for mammogram density classification. IEEE Access. doi.org/10.1109/ACCESS.2024.3399204.
[2]
Novel AI algorithm assists in breast cancer screening
Researchers at the University of Eastern Finland have developed a novel artificial intelligence-based algorithm, MV-DEFEAT, to improve mammogram density assessment. This development holds promise for transforming radiological practices by enabling more precise diagnoses. The study is published in IEEE Access. High breast tissue density is associated with an increased risk of breast cancer, and breast tissue density can be estimated from mammograms. The accurate assessment of mammograms is crucial for effective breast cancer screening, yet challenges such as variability in radiological evaluations and a global shortage of radiologists complicate these efforts. The MV-DEFEAT algorithm aims to address these issues by incorporating deep learning techniques that evaluate multiple mammogram views at the same time for mammogram density assessment, mirroring the decision-making process of radiologists. The research team involved with AI in cancer research consists of Doctoral Researcher Gudhe Raju, Professor Arto Mannermaa and Senior Researcher Hamid Behravan. In the present study, they employed an innovative multi-view deep evidential fusion approach. Their method leverages elements of the Dempster-Shafer evidential theory and subjective logic to assess mammogram images from multiple views, thus providing a more comprehensive analysis. MV-DEFEAT showed remarkable improvements over existing approaches. It demonstrates a significant improvement in mammogram screening accuracy by automatically and reliably quantifying the density and distribution of dense breast tissue within mammograms. For instance, in the public VinDr-Mammo dataset which consists of over 10,000 mammograms, the algorithm has achieved an impressive 50.78% improvement in distinguishing between benign and malignant tumors over the existing multi-view approach. Interestingly, the algorithm's effectiveness persisted across different datasets, reflecting its robust performance to adapt to diverse patient demographics. The study utilized extensive data from four open-source datasets, enhancing the algorithm's applicability and accuracy across different populations. Such capabilities underline the importance of AI-based approaches in medical diagnostics. Furthermore, while MV-DEFEAT significantly aids in breast cancer screening, the team at the University of Eastern Finland emphasizes the need for continued refinement and validation of the algorithm to ensure its reliability and efficacy in clinical settings. These promising results pave the way for the use of AI in enhancing diagnostic processes, potentially leading to earlier detection and better patient outcomes in breast cancer care. "To fully integrate AI like MV-DEFEAT into clinical practice, it is crucial to build trust among health care professionals through rigorous testing and validation. Indeed, our next steps involve further validation studies to establish MV-DEFEAT as a reliable tool for breast cancer diagnostics in Finland," says Doctoral Researcher Raju Gudhe of the University of Eastern Finland.
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A new AI-based algorithm has been developed to improve mammogram density assessment, potentially enhancing breast cancer screening accuracy and efficiency. This innovation could lead to more personalized screening approaches and better risk assessment.
Researchers have developed a groundbreaking artificial intelligence (AI) algorithm that promises to revolutionize breast cancer screening by improving mammogram density assessment. This innovative approach could lead to more accurate and efficient breast cancer detection, potentially saving lives through earlier diagnosis 1.
Breast density is a crucial factor in mammogram interpretation. Dense breast tissue can mask potential tumors, making it more challenging for radiologists to detect cancer. Additionally, women with dense breasts have a higher risk of developing breast cancer. The new AI algorithm aims to address these challenges by providing a more precise and consistent assessment of breast density 2.
The AI-based algorithm analyzes mammogram images to determine breast density more accurately than current methods. It uses advanced machine learning techniques to identify subtle patterns and features in the breast tissue that may be difficult for human observers to detect consistently. This automated approach could standardize density assessments across different healthcare providers and reduce variability in interpretations 1.
Implementing this AI algorithm in breast cancer screening programs could offer several advantages:
More accurate risk assessment: By providing a more precise measure of breast density, the algorithm could help identify women who may benefit from additional screening or preventive measures.
Personalized screening approaches: Healthcare providers could use the AI-generated density information to tailor screening protocols to individual patients, potentially increasing the detection rate of early-stage cancers.
Improved efficiency: The automated assessment could save time for radiologists, allowing them to focus on more complex cases and potentially reducing waiting times for results 2.
While the AI algorithm shows promise, researchers emphasize the need for further validation through clinical trials. Integration into existing healthcare systems and ensuring compatibility with various mammography equipment are also important considerations. Additionally, training healthcare professionals to work alongside AI tools will be crucial for successful implementation 1.
As this technology continues to develop, it may pave the way for more advanced AI applications in breast cancer screening, such as automated lesion detection or risk prediction models. The ultimate goal is to create a more effective and efficient breast cancer screening process that can save more lives through early detection and personalized care 2.
Reference
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A review article in Trends in Cancer highlights how artificial intelligence is revolutionizing breast cancer screening and risk prediction, offering potential for personalized screening strategies and improved early detection.
8 Sources
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A nationwide study in Germany shows AI-assisted mammography screening significantly improves breast cancer detection rates without increasing false positives, potentially revolutionizing breast cancer screening practices.
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A recent study reveals that AI can detect breast cancer risk up to six years before clinical diagnosis, potentially revolutionizing early detection and personalized screening approaches.
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A study reveals that AI-enhanced mammography screening could increase breast cancer detection rates by 21%, highlighting the potential of AI in improving early diagnosis and patient care in radiology.
4 Sources
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Researchers have developed a new AI-powered method to detect breast cancer by analyzing "zombie cells". This innovative approach promises improved accuracy and earlier detection of breast cancer, potentially revolutionizing diagnostic procedures.
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