The global statistics for breast cancer are staggering: 1 in 8 women worldwide will be diagnosed at some point during their lifetime.
As with all cancers, early detection is key.
Women who are diagnosed early have a 95 percent chance of living at least five years after diagnosis and it's estimated early breast cancer diagnosis could save 400,000 lives globally each year, according to the World Health Organization.
Fine needle aspiration (FNA) is currently the least invasive technique to biopsy breast lumps, or masses. During the procedure — about as painful as a blood test — a doctor retrieves enough cell tissue through a tiny needle for a microscopic analysis.
FNAs are less painful, less expensive to do, result in less complications for patients, and make results available more quickly than the current traditional core or open biopsies.
But here’s the problem. FNAs are currently less reliable in conclusively diagnosing breast cancer than more invasive and painful techniques.
As a result, many health organizations in the United Kingdom, the United States and Canada have now abandoned FNA for breast lesion diagnosis, although they are still used in nine countries worldwide.
What if there was a way to make these minimally invasive diagnostic tests more reliable? No doubt women everywhere would be cheering.
As it turns out, there is.
FNAs can be analyzed through computers equipped with artificial intelligence with a high degree of accuracy. Through the flexibility of cloud computing — a network of remote servers hosted on the Internet to store, manage, and process data — the ability for doctors globally to access the diagnostic tool using a tablet or smart phone now exists.
Meet Brittany Wenger of Lakewood Ranch, FL, the creator of the Global Neural Network Cloud Service for Breast Cancer, the tool that is making it all possible.
Wenger’s Artificial Neural Network (ANN) replicates the human brain in software by modeling neurons and their interconnections. With ANN, patterns too complex for a human or program to even notice can correctly detect breast cancer lesions 99.1 percent of the time.