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.
Right: A picture of the prototype which has been retrofitted into a typical clinical-grade light microscope. Applications of deep learning in medical disciplines including ophthalmology, dermatology, radiology and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare. To further this technology, Google researchers have developed a tool that combines augmented reality with a deep learning neural network to provide pathologists with help in spotting cancerous cells on slides under a microscope. The prototype Augmented Reality Microscope (ARM) platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. The ARM can be retrofitted into existing light microscopes found in hospitals and clinics by using low-cost, readily available components, and without the need for whole slide digital versions of the tissue being analyzed.
This is how a pathologist could save your life. Imagine you're coughing up blood, and a chest scan reveals a suspicious mass in your lungs. A surgeon removes a small cylindrical sample from the potential tumor, and the pathologist places very thin slices of the tissue on glass slides. After preserving and staining the tissue, the pathologist peers through a microscope and sees that the cells have the telltale signs of lung cancer. You start treatment before the tumor spreads and grows. And this is how a pathologist could kill you: The expert physician would just have to miss the cancer.
The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy. However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability. Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel. In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare. However, despite significant advances in AI research, integration of these tools into real-world cancer diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions. Here we propose a cost-effective solution to the integration of AI: the Augmented Reality Microscope (ARM). The ARM overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of AI into the regular microscopy workflow. We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows. We anticipate that ARM will remove barriers towards the use of AI in microscopic analysis and thus improve the accuracy and efficiency of cancer diagnosis. This approach is applicable to other microscopy tasks and AI algorithms in the life sciences and beyond.