Technology & Team

Utilizing leading deep-learning technology, we have already successfully applied our technology to the security, finance, transportation and smart device fields. Additionally, we are deeply involved in research to further develop the artificial intelligence field.

We have already created a complete data processing platform that can perform a variety of functions such as matching, cleaning, marking, training, mining as well as other skills. We have already collected vast amounts of medical data.


Yitu Smart Medical Imaging Platform

Disease Screening Assistance

Assist medical professionals in providing diagnoses for patients’ screened data. This feature includes accurately recording each appointment and providing smart analysis of the records. It also uses a well-established processing mechanism and grading scale to provide comments to the medical professional.

Imaging Diagnosis Assistance

Provide analysis for various medical imaging devices, including CT/X-Ray/MRI. This feature helps medical professionals to quickly locate regions of interest, and also creates a structured diagnosis report according to the imaging results.

Clinical Treatment Assistance

Assist clinical professionals in providing sketches for organ separation and radiation therapy regions to increase the accuracy and success rate of treatment.

Yitu Medical Research and Clinical Decision
Support Platform

Medical dictionary and text structuring: based on an industry standard medical dictionary and connecting with the hospital’s medical records, we can create a hospital’s own medical reference book. This can turn medical records into organized and usable data.

Research data analysis tool: analyze the data for a given range of patients

Diagnosis assistance: Use patient’s symptoms and other indicators to determine the probability of having a particular disease and find related historical data to support clinical decisions.

Customized disease prediction: create an individual patient model to predict disease development and medicine’s effectiveness.

Treatment plan assistance: Based on an individual patient model, incorporate medical insights to assist medical professionals in creating an effective treatment plan.

How We Work

Zhejiang Provincial People’s Hospital

Lung cancer is the most common form of cancer in China, and it also has the highest mortality rate. It is difficult to detect, so the prognosis is typically not good. Increasing early detection is critical to reducing the mortality rate of lung cancer. Zhejiang Provincial People’s Hospital uses Yitu’s smart medical imaging platform for lung cancer early detection.

The Yitu smart medical imaging platform applies deep learning technology to analyze tens of thousands of medical images. By accurately locating suspicious nodes, we can analyze their size, shape and location. Furthermore, our analysis can incorporate data from previous hospital visits to track changes in the condition. In this way we provide a complete range of assistance for medical professionals.

In clinical applications, the Yitu smart medical imaging platform provides accurate locating and quick analysis, greatly reducing work loads, increasing efficiency, as well as decreasing the number of inaccurate and missed diagnoses.

Guangzhou Women and Children’s
Medical Center

The national lack of pediatricians is a serious problem for China. Guangzhou Women and Children’s Medical Center have cooperated to produce the MiMu Bear smart diagnosis platform. This platform incorporates artificial intelligence to provide a preliminary diagnosis for patients and pediatricians.

MiMu Bear is particularly successful at diagnosing sicknesses related to fever. Fevers can be symptoms of many different sicknesses such as upper respiratory infection, angina, hand-foot-and-mouth disease, pneumonia and even more serious diseases such as leukemia and sepsis. The uncertain of what caused the fever is extremely distressing for parents.

MiMu Bear utilizes deep learning technology to process historical medical records. It is able to create disease diagnosis models, unique symptoms models, and models based on similar medical history. At the same time, through user feedback, it can automatically improve its calculations to increase its modeling power.