Augmenting Mammography with Artificial Intelligence
Support the daily work at the breast unit by Artifical Intelligence
- Artificial Intelligence mimicking doctor decisions.
- Real time Feedback.
- Automatic classifications according to BI-RADS ACR.
- Image Quality classification according to PGMI.
- High clinical accuracy.
- Easily adaptable to each clinical site.
The Problem
Early detection of breast cancer by mammography allows more treatment options and reduction in mortality. However, catching cancer is difficult in patients with a dense breast tissue and in poor quality images.
Our Solution
Empowered by Artifical Intelligence the b-box, supports the daily work of the radiologist and the technician.
Density
High breast density is one of the most important risk factors for breast cancer development. Moreover, the sensitivity of mammography for the detection of breast cancer dramatically drops from 98% for thin breasts to only 48% for dense breasts. For high breast density, additional ultrasound examination for breast cancer screening is recommended. The classification of the breast density is based on the visual rating of the doctor and thus extremely physician dependent. Poor reproducibility of the density determination results in potential misestimation of cancer risk and missed breast ultrasound examinations. Our software provides standardized, reproducible, and real-time classification of ACR BIRADS breast density with a proven accuracy over 93%.
Quality
Our software provides real time information on the PGMI based quality assessment of mammographies. Using this solution, assessment of image quality can be carried out close to the mammography unit directly after image acquisition. The detailed feedback helps the technician correcting recurrent errors in the positioning of the patients in the mammography scanner. Moreover, as the obtained PGMI score can be linked to the technician performing the acquisition of the mammography, individual performance measurements including systematic errors as a quality control tool are feasible. The automation of the quality assurance allows for the first time tracking of the quality performance on the whole volume of data.
Density
High breast density is one of the most important risk factors for breast cancer development. Moreover, the sensitivity of mammography for the detection of breast cancer dramatically drops from 98% for thin breasts to only 48% for dense breasts. For high breast density, additional ultrasound examination for breast cancer screening is recommended. The classification of the breast density is based on the visual rating of the doctor and thus extremely physician dependent. Poor reproducibility of the density determination results in potential misestimation of cancer risk and missed breast ultrasound examinations. Our software provides standardized, reproducible, and real-time classification of ACR BIRADS breast density with a proven accuracy over 93%.
Quality
Our software provides real time information on the PGMI based quality assessment of mammographies. Using this solution, assessment of image quality can be carried out close to the mammography unit directly after image acquisition. The detailed feedback helps the technician correcting recurrent errors in the positioning of the patients in the mammography scanner. Moreover, as the obtained PGMI score can be linked to the technician performing the acquisition of the mammography, individual performance measurements including systematic errors as a quality control tool are feasible. The automation of the quality assurance allows for the first time tracking of the quality performance on the whole volume of data.
What our Customers say...
"Automatized quality checks support my visual inspection. I have more time for my patients and better imaging data for diagnosis."
"An intelligent tool targeting a compelling need in mammography. The b-box meets our goal of providing excellent care to every patient."
"A work and educational support for the technical specialist on the front line."
