We are training our proven, proprietary algorithm to accurately and quickly detect and classify anomalies in mammograms to enable early detection of breast cancer.
WE ARE WORKING TO SAVE LIVES. The CureMetrix learning algorithm quickly and accurately detects anomalies in very noisy 2D and 3D images. Unlike traditional techniques such as neural networks, support vector machines, and computer vision, we can use our algorithm to explore images with more accuracy and understanding of the outcomes.
Accurately and consistently detecting and diagnosing breast cancer using mammograms continues to be challenging with false negative rates each hovering at around 20%. CureMetrix is working with esteemed institutions such as UCSD and Tijuana General Hospital to train our algorithm to recognize different types of anomalies in mammograms and correctly classify them as benign or malignant. Our goal is to be able to consistently, accurately, and quickly be able to assist radiologists with their diagnosis. Ultimately this will help reduce over diagnosis, reduce unnecessary procedures, reduce anxiety in patients, and save lives.
And beyond mammograms are the other 600 million medical imaging studies conducted every year in the US!
The standard procedure for both radiotherapy and surgery involves making scans and planning treatment prior to the operation. In the mean time, organs can shift, the patient breaths, and occasionally the alignment during the scan and operation do not match completely. For many situations these shifts are not important, but around sensitive organs and small tumors these can mean the difference between recovery and relapse.
The latest surgical suites and MRI technologies have made subsecond measurements a reality. These measurements are already being used to assist therapy, but the value is limited without the ability to process the images in real-time.