We Can Predict one’s Brain Tumor Development
The core of the technology is to provide end-to-end and generalized solutions for predicting the prognosis of metastatic brain tumors as rapid brain metastasis and subsequent medical procedures are critical. Human factors such as physician experience, habits, and fatigue have also added many possible errors to the process of brain metastasis. The personalized tumor labeling system will create an automated and standardized brain tumor labeling process, and provide a personalized elastic space for brain tumor contouring. The deep learning method is used to construct a recurrence prediction from the image factor and structured clinical data of the MRI image. The model can assist physicians in the choice of treatment options, and provides a Domain Learning module for Transfer Learning to provide self-learning of machine learning programs, allowing the client to optimize the predictive program based on patient characteristics.
This is a whole body image identification engine. As large quantities amount of physical body slices images are in the database, when an image is uploaded to the database, the system will be able to recognize which part of the body the image is from. This body part or organ imaging source recognition can assist physicians to reduce time on analyzing images and where they are from when a patient with multiple diseases’ imagings need to be read.
This pathology software will be able to reconstruct 2D pathology images to 3D sphere at a cellular level and will allow physicians to better understand pathological imaging results.
Prostate Gleason Score Classification
This tool is able to identify a patient’s prostate condition based on Gleason score in seconds. By outputting two numbers, for example 3+4, it tells us that most of this patient’s prostate has a Gleason score of 3, while the second largest amount of tissues are scored with 4. Physicians no longer need to spend time on the Gleason score classfication.
The Smart ED project focuses on the development of two tools that will assist ED physicians by reducing time required for decision making process. The first tool is a portable x-ray that can be used by the bedside on patients in various positions. By quickly identifying TB and other diseases or reducing number of attempts for intubations, the portable x-ray will be an effective in the ED setting. The second tool is to quickly understand patients’ brain activities and the grey/white matters’ changes that might indicate the result of different brain surgeries. This tool can effectively help physicians to determine whether a patient’s condition is suitable for various surgeries.
By teaming up with world class Statisticians, the large data sets of 80,000 physical examinations done at the National Taiwan University Hospital over the course of 10 years were able to generate clinical causations by looking at specific indicators. Physicians will be able to utilize these population specific inferences to assist patients when interpreting their physical check-ups results.