Abstract: Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, underscoring the critical need for early and accurate diagnostic methodologies. This review describes an advanced breast cancer detection system that integrates Artificial Intelligence (AI) with sophisticated imaging mechanisms to improve diagnostic precision. The system employs highresolution imaging technology to non-invasively visualize and isolate successive layers of breast tissue, thereby providing detailed, multi-planar views of targeted anatomical regions. This approach facilitates deeper examination of tissue architecture. Through subsequent AI-driven computational analysis, potential malignant areas, including microcalcifications and subtle structural distortions, are identified and highlighted in real time. This integrated approach enables faster and more reliable interpretation of complex imaging data while reducing the cognitive burden on radiologists. The core mechanism enhances diagnostic accuracy by detecting aberrant tissue patterns and generating comprehensive layered insights, including quantitative assessments of tissue density and vascularization, to support clinical decision-making and risk stratification. By combining depth-resolved visualization with automated interpretation, this approach has the potential to advance beyond conventional twodimensional screening paradigms. The integration of AI and advanced imaging contributes to a more sensitive and specific diagnostic workflow. Consequently, this review presents SDSND as a hypothetical and emerging framework integrating AI and nanoscale detection. However, its clinical applicability remains unvalidated and requires experimental and clinical investigation.
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