It just needs to use the photo from the first stage H&E staining, and it can generate the results in a short time by the AI model, established by the team. At the same time, it will also generate the standard medical report. It is different from the past. In traditionally, pathologists used to stain the biopsy with H&E and IHC step by step. It would spend about 3~5 days for the result. By using the system to replace the second stage IHC staining, it reduces the time to be 1 day. It can save the time. And promote the accuracy rate with objective judgment.

In recent years, the incidence of breast cancer in country is increasing year by year, and it is the second leading cause of death among women. Once diagnosed with breast cancer, in addition to the outpatient physician, the patients should know what type of the cancer they have In order to know which treatment is more effective for them. H&E and IHC are currently the most common breast cancer detection techniques. Doctors also diagnosis and treatment based on the test result. IHC is an examination that takes a long time and is not cheap, and there is also the possibility of human observation errors. Based on this, Professor Wang and Professor Hsu developed the system for accelerating and accuracy of the detection process.

It is difference between the breast cancer cells and normal cancer cells. The breast cancer will grow quickly, influenced by hormones, especially with hormones receptors on the cells surface. Hormones receptors can be recognized to ER and PR. The treatment and prognosis will effect better while there are more hormones receptors in breast cancer cells. Therefore, it is necessary to examine the quantity of hormones receptors for breast cancer, then decide to which treatment method be better. Two-stage examination is often used by pathologists so far. It wastes time and money in the second examination especially.

In addition, this system not only includes factors ER and PR, but also adds human epidermal growth factor receptor 2 (HER2) and Ki-67. The detection accuracy of breast cancer types is greatly improved, allowing patients to receive more precise treatment and improving the survival rate of patients

Currently, NCU team is actively cooperating with Mackay Hospital. Adjust the system according to the hospital environment and doctor's suggestions, and combined with the hospital's internal system. It can generate reliable medical reports, reduce the burden of current and future hospital personnel needs, and allow hospital medical resources to be used more effectively where they are needed by achieving AI automation. So as to achieve the goal of industry-university cooperation.

1.Quickly
2.Less expense
3.Objective judgment
4.Higher accuracy
5.Auto report