New solution to accelerate digital pathology
Digital pathology opens a new era in cancer diagnosis. However, barriers still exist to the full implementation of digital pathology. The preparation of fine needle aspiration (FNA) or tissue biopsy samples performed for traditional cancer diagnosis is a labor -intensive and complicated procedure. In addition to the digital imaging process using digital scanners, you need to implement an accurate and consistent sample preparation process. Noul offers a new smart staining technology for FNA and biopsy morphological analysis. Even given different types of samples, you can always perform consistent staining. This sample preparation technology will be an innovative technology that can increase the accuracy of diagnosis by integrating with artificial intelligence-based cancer diagnosis technology.
Smart H&E staining method
Tissue staining is a very sophisticated procedure. Depending on skilled technicians, the quality of sample preparations may vary continuously. In addition, the appropriate staining protocol for the types of tissue should be followed. However, it is a realistically impossible situation. Noul’s repetitive stamping technology through NGSI is an innovative technology that can maintain stable staining quality at anytime simply and quickly.
Traditional H&E staining protocol vs 3-Step H&E NGSI
The conventional method is a at least 8 steps and takes about 15 minutes, as shown in the table below, but noul’s 3-step H&E NGSI method is a simple stamping method using 3 patches and is completed within 3 minutes.
VS 3-step stamping
H&E NGSI-stained FFPE Breast Tissue
Portable FNA-based cancer diagnosis platform
In cancer diagnosis, FNA is a very efficient and safer way to take cancer samples quickly and easily, with the advantage of being minimally invasive and less side effects using a thin diameter needle. FNA is used as a standard test for rapid on-site-evaluation (ROSE) of pancreatic and lung cancer in the laboratory, as well as for the diagnosis of thyroid and breast cancer. However, the complexity of preparing samples for FNA analysis has the limitation that there must always be a long processing time of experienced experts and pathologists for diagnosis.
Therefore, to maximize the benefits of FNA analysis, as well as overcome the limitations of the cancer diagnosis process, we develop an intelligent, fully automated cell analysis system from sample preparation to AI diagnosis. This product can be applied to the point of care cancer diagnostics and digital ROSE for on-site cancer diagnosis in the operation rooms.