Detecting whether cancer has spread, or metastasized from the primary area to the lymph nodes is not an easy task and it is a very time-consuming process as well for pathologists. Google aims to provide scientists with an option to simplify this process by providing artificial intelligence that can help detect and fight this disease.
In a recent blog post by researchers at Google, they explain that their AI which is known as LYNA (abbreviated from LYmph Node Assistant) has made significant headway and is now capable of recognising the difference between cancer and non-cancer on slides with an astounding 99 per cent accuracy in the right conditions even while looking for small metastases that human beings might miss.
Google states that LYNA was not perfect and often misidentified giant cells, germinal cancers, and bone marrow-derived white blood cells known as histiocytes. However, it has to be noted that it fared better than most pathologists. Google goes on to say that pathologist’s diagnosis of lymph nodes metastases can be quite inaccurate due to time constraints and fast decision making in the early stages of treatment. While referencing past research, Google states that the accuracy of an individual slide can be as low as 38 per cent.
Google states, “LYNA was able to accurately pinpoint the location of both cancers and other suspicious regions within each slide, some of which were too small to be consistently detected by pathologists. As such, we reasoned that one potential benefit of LYNA could be to highlight these areas of concern for pathologists to review and determine the final diagnosis.”
Google states that this suggests the intriguing potential for assistive technologies such as LYNA to reduce the burden of repetitive identification tasks and to allow more time and energy for pathologists to focus on other, more challenging clinical and diagnostic tasks. In terms of diagnostic accuracy, pathologists in this study were able to more reliably detect micrometastases with LYNA, reducing the rate of missed micrometastases by a factor of two.
Google has made progress in demonstrating the robustness of our LYNA algorithm to support one component of breast cancer TNM staging and assessing its impact in a proof-of-concept diagnostic setting.
Google ends by stating that they remain optimistic that carefully validated deep learning technologies and well-designed clinical tools can help improve both the accuracy and availability of pathologic diagnosis around the world.