Artificial Intelligence in Breast Cancer Screening in Inducing Diagnostic Accuracy with Early Detection

Amiya Kumar Prusty¹_, Prasanna Parida², Pradip Kumar Prusty³, Suryakanta Swain⁴, Bikash Ranjan Jena^2*, Ram Naresh Yadav⁵, Sanjoy Das⁶, Bhisma Narayan Ratha⁷

This review discusses the AI to improve diagnostic accuracy in breast cancer screening, potentially reducing false positives and negatives and improving patient outcomes.

Abstract

Background: The integration of artificial intelligence (AI) into breast cancer screening has shown promise in overcoming these limitations, potentially improving diagnostic accuracy and patient outcomes. Methods: This study reviews the current imaging modalities for breast cancer detection, including mammography, magnetic resonance imaging (MRI), dynamic contrast-enhanced MRI (DCE-MRI), magnetic resonance elastography (MRE), magnetic resonance spectroscopy (MRS), positron emission tomography-computed tomography (PET-CT), ultrasound, breast-specific gamma imaging (BSGI), and molecular image-guided sentinel node biopsy. It also explores the role of AI in enhancing diagnostic precision through advanced computational techniques, such as deep learning and machine learning models, focusing on object detection, segmentation, and tumor classification. Results: Mammography remains the gold standard for breast cancer detection but poses risks, such as overdiagnosis and radiation exposure. Alternative methods, like MRI, DCE-MRI, and BSGI, offer advantages in specific scenarios but also have limitations, such as low specificity and higher costs. AI systems have demonstrated superior performance in breast cancer prediction compared to human experts by reducing false-positive and false-negative rates. AI integration enhances screening programs by improving the detection accuracy of imaging biomarkers and facilitating automated interpretation. Conclusion: The incorporation of AI in breast cancer screening represents a significant advancement in early detection and diagnosis, improving treatment outcomes.

Keywords: Breast cancer screening, Artificial intelligence (AI), Imaging modalities, Diagnostic accuracy, Machine learning

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