Izotropic Corporation has announced a major advancement in breast CT imaging with the integration of its proprietary AI-powered machine-learning reconstruction algorithm into the IzoView Breast CT Imaging System. This breakthrough positions IzoView to redefine global standards for image quality and patient safety in breast cancer screening. Developed in collaboration with The Johns Hopkins University School of Medicine, the algorithm is a self-supervised deep learning method trained on 15 years of specialized breast CT data. Unlike conventional AI denoising techniques that operate after image reconstruction, IzoView’s algorithm works directly on raw X-ray data, significantly reducing image noise while maintaining anatomical detail and preserving the natural texture of breast tissue. In CT imaging, noise appears as graininess and increases when radiation dose is lowered—a critical concern for patient safety. While existing methods like Model-Based Iterative Reconstruction (MBIR) and Deep Machine-Learning Reconstruction (DMLR) have improved image quality, they face major limitations. MBIR is extremely slow, often requiring minutes per scan, and demands substantial computing power, making it impractical for high-throughput screening. DMLR methods typically rely on paired high- and low-dose scans for training, which increases patient radiation exposure. Even single-dose approaches struggle with the complex, correlated noise patterns typical in breast CT, often leading to over-smoothing and loss of subtle diagnostic features. IzoView’s algorithm overcomes these challenges by enabling high-quality image reconstruction at low radiation doses without requiring paired training data or excessive processing time. This makes it ideal for routine clinical use in breast cancer screening, where speed, accuracy, and safety are paramount. The algorithm is protected as a trade secret, giving Izotropic a durable competitive advantage. Its deep specialization in breast CT—built on a unique, decade-long dataset—makes replication difficult and costly for competitors. As general-purpose AI models become more common, tailored, modality-specific innovations like this are emerging as key differentiators in medical imaging. By producing clean, high-resolution images optimized for both radiologists and AI systems, IzoView creates an ideal foundation for future computer-aided diagnostics. As AI-driven diagnostics become central to precision medicine, the quality of input imaging data will be critical. IzoView’s ability to deliver high-fidelity images at low dose supports both clinical accuracy and regulatory compliance. Izotropic believes this advancement could set a new benchmark for breast CT imaging worldwide. While the IzoView system has not yet received regulatory approval or clearance for sale, the integration of this AI technology marks a significant step toward transforming breast cancer screening with safer, smarter, and more accurate imaging.