Recently, company interns or the media often ask about investment ideas in medical hotspots (such as artificial intelligence, medical robots, precision medicine or VR). The medical field has always been considered conservative and closed, but investment is often chasing and Like hot spots. At present, medical investment is also a hot spot in the investment circle, and the hotspots in the medical field are naturally regarded as hot spots in the hot spot. It is quite reasonable to draw attention from everyone. How to make hot investment in the conservative industry characteristics is an interesting topic. This article will start with the industry attributes and explore the law to find investment opportunities.
Artificial intelligence has been used in medical treatment for a long time, and foreign countries have already commercialized it.
Artificial intelligence as a terminology was proposed in the summer of 1956, and over 50 years, it has made great progress and become a wide-ranging crossover and cutting-edge science.
In the medical field, we are very familiar with IBM Watson who has been working for the MD Cancer Center for a while, providing valuable information to clinicians through Watson's cognitive computing capabilities, looking for information from patient cases and a rich research database. Insights to help healthcare professionals find the most effective treatment options. Artificial intelligence medical also includes Enlitic, AtomwiseAtomwise, The Human Diagnosis Project, Butterfly Network, Arterys, and more. However, the particularity of medical treatment determines that any product needs to undergo a rigorous approval process before commercial application. In fact, most of these famous products are still far away from us. We expect them to pass the approval as soon as possible and enter our lives.
The application of artificial intelligence in the medical field is currently not available for treatment, and its application in diagnosis has been 40 years old, namely computerized diagnosis (CAD). The CAD technology that is often said at present mainly refers to computer-aided technology based on medical imaging. Since the 1990s, the rapid development of artificial neural networks (ANN) has made a qualitative breakthrough in CAD in the field of medical imaging. It is a mathematical processing method that mimics the working principle of human brain neurons. Because of its self-learning ability, memory ability, and predictive event development, it can play a role in assisting diagnosis. In terms of classification and diagnosis, artificial neural network methods are more traditional than traditional methods (probability statistics, mathematical models, etc.). Superior performance. At present, CAD research is mostly limited to breast and thoracic pulmonary nodular lesions. Therefore, CAD studies of breast and pulmonary nodule lesions can basically represent the highest level and current status of CAD in medical imaging. Foreign commercial applications are concentrated in these two areas. For example, R2, founded in Silicon Valley in 1994, is the world's first FDA-approved (97) breast cancer mammography computer-aided diagnostic system with cumulative sales of more than $1 billion (later acquired by Hologic).
Therefore, CAD is the earliest and most representative application of artificial intelligence in the medical field, and its application in the diagnosis of breast diseases has achieved a wide range of commercialization.
The commercial application of overseas breast CAD has not affected our lives.
For example, in the most mature breast diseases in the field of CAD commercialization, breast cancer screening methods include breast self-examination, clinical examination (CBE), ultrasonography, mammography, tumor marker examination, and gene sequencing diagnosis. Due to the lack of individual differences and the lack of professional knowledge, the detection rate of breast self-test is low, and it is not suitable for general survey. It is only an auxiliary means for women to pay attention to self-health. The tumor markers of breast cancer are mainly mucin-like glycoprotein MUC-1 family, such as CA15-3, cancer embryo antigen CEA, oncogene HER-2 or CerbB-2, milk protein, glycolytic enzyme and cytokeratin, tissue Polypeptide specific antigen. The clinical application of tumor markers is mainly the detection and prevention of breast cancer metastasis, recurrence and prognosis. It lacks sensitivity and specificity for early diagnosis of breast cancer. Therefore, tumor markers have little significance for early diagnosis of breast cancer. The use of gene sequencing for early screening of breast cancer is currently immature, and the complexity of tumor formation also means that the genetic knowledge we have mastered cannot fully explain the whole process of development, gene sequencing for a long time. It is not a substitute for imaging screening, but with the development of gene sequencing technology, there are still possibilities for screening it for breast cancer, but it is estimated that the traditional method will remain dominant in the next 10-15 years.
The current screening methods are mainly clinical examination (CBE), ultrasound examination and mammography. The current national census, such as the “two cancer†screening project initiated by the Ministry of Health, is generally based on ultrasound examination, which is based on the more economical and radiation-free conditions of ultrasound examination than mammography. . At the same time, because of the large number of women in China, the use of mammography for the use of dense mammography has a high rate of missed detection.
Comparison of basic situation of breast cancer between China and foreign countries
Source: 2015 China Cancer Statistics
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