BreastScreening-AI offers an AI platform that automatically analyzes mammograms, breast MRI, and ultrasound images using multimodality convolutional neural networks to detect and classify lesions. The system provides visual overlays, confidence scores, and explainable heatmaps as a reliable second opinion that integrates seamlessly with existing PACS workflows, helping radiologists improve early cancer detection and reduce diagnostic errors.
Funding
Funding not disclosed
Founders
Product
Problem
Radiologists often face diagnostic uncertainty and fatigue when interpreting large volumes of breast imaging studies, leading to missed lesions and variable patient outcomes. Access to consistent, high‑accuracy second opinions is limited, especially in busy or resource‑constrained settings.
Solution
BreastScreening‑AI provides an artificial‑intelligence platform that automatically analyzes mammograms, breast MRI, and ultrasound images using multimodality convolutional neural networks (MMCNNs). The system generates lesion detection and classification results that can be reviewed alongside the original images, offering a reliable second opinion to support radiologists’ decisions. By consolidating multiple imaging modalities into a single model, the AI delivers comprehensive assessments that improve early cancer detection while reducing the risk of human error. The solution integrates with existing picture‑archiving and communication systems (PACS), enabling seamless workflow adoption without extensive infrastructure changes.
Target Audience
Primary customers are radiology departments, breast imaging centers, and diagnostic clinics that perform mammography, breast MRI, or ultrasound examinations.
Features
- Multimodality CNNs capable of jointly processing mammography, MRI, and ultrasound data for unified cancer detection
- Automated lesion localization and malignancy scoring presented as overlay visualizations on original images
- Confidence metrics and explainable AI heatmaps to aid radiologist interpretation and trust
- Compatibility with standard DICOM and PACS workflows for easy integration into clinical environments
- Continuous model updates driven by a curated, anonymized imaging database to maintain state‑of‑the‑art performance