Houston, Texas, USA: Google on Friday claimed that its AI algorithm can assist doctors in metastatic breast cancer detection with 99 percent accuracy, according to their papers published in the Archives of Pathology and Laboratory Medicine and The American Journal of Surgical Pathology. The algorithm technology, known as Lymph Node Assistant, or LYNA, is taught to check the abnormality in the pathology slides and accurately pinpoint the location of both cancers and other suspicious regions since some of the potential risks are too small to be spotted by the doctors. In their latest research, Google applied LYNA to a de-identified dataset from both Camelyon Challenge and an independent dataset from the Naval Medical Center San Diego for picking up the cancer cells from the tissue images. Metastatic tumors -- cancerous cells which break away from their tissue of origin, travel through the body through the circulatory or lymph systems, and form new tumors in other parts of the body -- are notoriously difficult to detect. A 2009 study of 102 breast cancer patients at two Boston health centers found that one in four were affected by the "process of care" failures such as inadequate physical examinations and incomplete diagnostic tests.
A 2009 study of 102 breast cancer patients at two Boston health centers found that one in four were affected by the "process of care" failures such as inadequate physical examinations and incomplete diagnostic tests. That's one of the reasons that of the half a million deaths worldwide caused by breast cancer, an estimated 90 percent are the result of metastasis. But researchers at the Naval Medical Center San Diego and Google AI, a division within Google dedicated to artificial intelligence (AI) research, have developed a promising solution employing cancer-detecting algorithms that autonomously evaluate lymph node biopsies. Their AI system -- dubbed Lymph Node Assistant, or LYNA -- is described in a paper titled "Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection," published in The American Journal of Surgical Pathology. In tests, it achieved an area under the receiver operating characteristic (AUC) -- a measure of detection accuracy -- of 99 percent.