Based on the high demand of professional interpreters in pathology and immune serum examination. The DigiPath Slide Scanner of Shuz Tung is to solve the problem of clinical scientist shortage. DigiPath Slide Scanner can provide digital slide fluorescence scanning with auxiliary Helicobacter pylori automatic detection and marker. The digital Slide image generated by DigiPath Slide Scanner is stored in the cloud to assist physicians in multi-consultation. Shuz Tung are committed to research and development to integrate domestic industry and medical cross-professional cooperation.

＊Customized expanded scanning system:
With our cassette design of the synchronous support standard slide(1 inch * 3 inch) and large slide(2 inch * 3 inch) ,it can provide hospitals with digitization and fully automated job scheduling of large tissue slides (e.g. prostate cancer slides).
Our immunofluorescent antinuclear antibody tests is to take 10 to 30 images as samples and provide a customized automated inspection process to improve the efficiency of quickly finding correct image in fluorescent interpretation.

＊Exclusive hardware and software integration system
The photographed image data will be uploaded to the cloud pathology image management platform after being confirmed by the image quality workstation. Clinical scientists can mark the data through the pathological image marking platform. To illustrate, the data will be pre-labeled by AI intelligence or clinical scientists, and then verified twice by physicians. The AI deep learning inference platform will choose the corresponding data and modules to perform model inference training. After the training and inference quality being confirmed, it can be directly connected to the hospital's integrated HIS&AI reporting system.

＊AI deep learning inference platform with extended features
At present, all the solution marking platform, deep learning training platform, and deep learning inference platform on the market are bound together. However, different medical images must be matched with suitable models to make inferences on the application of AI automatic detection.
Different manufacturers may have their own specialized models. To view from the hospitals' standpoint, they will expect that the inferential platform deployed in the future will be compatible with different models trained by their own hospital, different vendors, or research institution, rather than being limited to a single vendor.