From 2011 to 2016, the annual average compound growth rate of China's medical technology and medical device industry revenue was as high as 20.7%, much higher than the global average compound growth rate of about 3%. In 2016, the market scale of China's medical technology and medical devices was about 370 billion yuan, an increase of 62 billion yuan over 308 billion yuan in 2015, with an annual growth rate of about 20.1%. It is estimated that the market scale will reach about 600 billion in 2019.
Among them, imaging equipment, in vitro diagnosis and high-value consumables occupy the top three segments of the medical technology and medical device market, accounting for 19%, 16% and 13% of the total market scale respectively. From the perspective of drug equipment ratio, the scale ratio of China's device market to the pharmaceutical market is only about 1:7, far lower than the global level of 1:3. From the per capita cost of medical technology and medical devices, the per capita cost in China is only $6, while the per capita cost in developed countries is more than $100, reaching $329 / person in the United States and $513 / person in Switzerland. Therefore, China's medical technology and device market has huge growth space both in terms of diagnosis and treatment methodology and consumption level.
In June 2016, the general office of the State Council officially issued the guidance on promoting and standardizing the application and development of health care big data, defined health care big data as an important national basic strategic resource, and incorporated the application and development of health care big data into the national big data strategic layout. The national health and health conference held in August pointed out that it is necessary to improve the construction of population health information service system and promote the application of health care big data.
Tumor is a new organism formed by abnormal proliferation and differentiation due to the loss of normal regulation of local tissue cells at the gene level under the action of various tumorigenic factors. Once a new organism is formed, it does not stop growing due to the elimination of the cause. Its growth is not regulated by the normal body physiology, but destroys the normal tissues and organs
Tumor radiotherapy is a local treatment of tumor by radiotherapy. Radiation, including that produced by Radioisotopes; X-rays and X-rays, electron lines, proton beams and other particle beams generated by various X-ray therapeutic machines or accelerators. About 70% of cancer patients need radiotherapy in the process of cancer treatment, and about 40% of cancer can be cured by radiotherapy. Radiotherapy plays an increasingly prominent role in tumor treatment and has become one of the main means of treating malignant tumors
By establishing and simulating the neural network of analytical learning of human brain and imitating the learning mechanism of human brain, it can automatically process data and assist and replace people to complete the task of high-intensity human-computer interaction under the training of data. We apply deep learning technology to medical image processing and tumor radiotherapy to solve the following pain points:
① Based on SaaS cloud service mode, doctors in different regions can conduct business processing through cloud services, and grass-roots hospitals enjoy the level of medical services of front-line hospitals
② Artificial intelligence automatic processing: improve the work efficiency of hospital doctors, reduce the misdiagnosis rate and reduce side effects
③ Reduce the treatment cost of patients, improve the number of patients in the hospital, and increase the treatment survival rate of patients
Medical imaging + artificial intelligence
It is an essential step for radiotherapy to outline the tumor and each organs at risk in the medical image. The deep learning algorithm enables the software to learn the outline of the target area and organs at risk in the selected database, so as to realize the automatic image segmentation of the medical image. The software will automatically segment the target area and organs at risk on the patient image. Doctors only need to review, fine-tune and modify the automatically segmented image, which greatly reduces the workload of doctors
Radiotherapy + artificial intelligence
The combination of big data and deep learning can effectively use the radiotherapy planning system accumulated in the past, automatically search out past cases similar to the current cases, and analyze how the radiotherapy plan on these medical records is designed and how the curative effect is through machine learning. Analyzing these cases through deep learning method can automatically complete the design of radiotherapy plan and automatically give appropriate radiotherapy plan. Unfortunately, at present, these large amounts of data are only stored and not used effectively. Learning a large amount of data by using machines can replace doctors to complete this work. Physicists only need to review and fine-tune the radiotherapy plan automatically completed by the software, so they can quickly complete the design of radiotherapy plan.