[The commercialization of artificial intelligence for medical images still has a long way to go] The deep learning algorithm based on convolutional neural networks has gradually become a new kinetic energy for medical reform since 2012. Based on CT, MRI, X-ray, ultrasound, thermal infrared, cell smear, electrocardiogram and other medical image of the intelligent auxiliary diagnosis and treatment system, has been proven effective in clinical use.
Not long ago, in a chat with Zou Guowen, a partner of Dao Investment, Mr. Zou disclosed that there are more than 100 companies in China that are dedicated to the development of intelligent auxiliary diagnosis and treatment systems. With so many entrepreneurs or entrepreneurs getting out of this, on the one hand it proves its great contribution to medical advancement, and on the other hand it also indicates its potential and huge commercial value space.
The virtual reality behind the investment outlet is basically clear in 2018. Some couldn't get financing and the capital chain was broken. Some of them had already been exposed. Some of them had been deeply practising medical care for many years, and they still struggled to do AI innovation. Some rely on technology, capital, and medical resources. The leader of the strength is still leading. The general market pattern is basically stable this year, leaving only a few companies in every subdivision.
If we are still fighting the accuracy of algorithm and image recognition in 2017, the fight in 2018 is to get certified and commercialized. How the product sells money will become the company's key research topic this year. Since last year, the author has talked with many entrepreneurs and investors about related topics, and has also formed some inconsistent views and opinions. I hope to share and discuss with the experts here.
I. Current mainstream products in the market
Pulmonary nodule identification and diagnosis is the starting point for most enterprise algorithm model training. On the one hand, it is because China is a big country with lung cancer. The volume of lung image data is the most abundant; on the other hand, the research on lung nodule recognition in the world is the most mature. The rapid progress in the diagnosis and screening of lung cancer in China is mainly attributed to science and technology, Tuma Shenwei, Jianpei Technology, Voxel Technology, Yitu Medical, Huihui Huiying, Shenrui Medical, Vision Medical, and China Resources Vanguard Cloud, Zero Technology, etc. In addition to lung cancer, automatic detection of breast cancer, liver cancer, nasopharyngeal cancer, and stroke is also becoming the focus.
Intelligent CT-assisted screening product (AI-CT) function interface
Cervical cancer assisted screening, the cervical smear-assisted screening system, is an area that is attracting industry attention. This application is mainly aimed at the pathology department and assists pathologists in completing the automatic detection of cellular DNA on pathological sections. Typical companies are Landing Medical, DeepCare. The former's core product, the Cellular DNA Autodetection Analyzer, is the earliest product that received the Type II certificate in China.
Diagnosis and screening of diabetic retinopathy, cases are also increasing. The voxel technology, Airdoc, DeepCare and other companies have launched related products.
There are also a few applications that are less involved in the enterprise:
· Tencent’s hard screening of esophageal cancer screening products;
· AI-ECG-Platform, introduced by Lepu Medical, is an automatic analysis and diagnostic product for ECG;
· The fracture X-ray intelligent diagnosis system launched by Huihui Huiying;
• The diagnostic screening of neonatal jaundice launched by Paula is an AI product for skin images.
In addition to the diagnostic link, there are also products worth looking forward to in the treatment session. In the field of radiotherapy, companies such as Lianxin Medical, Huihui Huiying, and Global Medical have introduced automatic target area delineation systems that can help doctors complete their radiotherapy plans efficiently and accurately.
In the field of surgery, companies such as Qijian Medical and China Resources Wanliyun have introduced image-based 3D reconstruction systems based on evolutionary algorithms and VR technology to help doctors plan preoperatively more accurately.
Second, the starting point in the top three, the end of the grass-roots
The training of the algorithm model requires a large amount of high-quality, multi-dimensional image data. These data are the largest in the top three hospitals. Therefore, enterprises have sought to cooperate with the top-three hospitals through scientific research and cooperation, and have obtained high-quality data. This has become the basic line. However, there is no shortage of high-quality imaging and radiotherapy doctors in the top three hospitals, and the primary hospital lacks. Therefore, from the perspective of practical application, the real demand is at the grassroots level and not at the top three. The medical informationization, grading diagnosis and treatment, and telemedicine that the state has vigorously promoted in the past two years are a major positive factor. Based on digital images and real-time interoperability of medical records in multi-level hospitals, primary hospitals will have the capacity to diagnose and treat major diseases such as cancer.
Many companies build their products on the cloud. From a private cloud in a hospital to a public cloud in many hospitals, it is the product form that will be used for future applications.
Third, do not do hardware?
Most of the companies mentioned above started as software systems. There are two kinds of profit models for software: First, they are sold to hospitals, and they are charged an annual fee. Second, they work with hospitals to charge patients. However, the financial models of these two profit models are still unknown. With the mainstream companies getting certification and entering the stage of marketization, domestic medical device manufacturers are likely to extend their “olives†and seek cooperation or mergers and acquisitions. The combination of the two, for start-up companies, can rely on the resource strength of medical device manufacturers and the high value-added services of hardware and software integration to quickly find a path for income generation; for medical device manufacturers, it can make the added value of products higher. , but also enhance the technical strength and market competitiveness of enterprises.
Create a company to do their own hardware? This is a question worth considering carefully. The advantage of doing hardware is to make money from consumables. In the medical device market, it is not the number of devices sold but the consumables that really make money. Taking Landing Medical as an example, in 2017, it will have a revenue of nearly RMB 100 million. The products it relies on are not only the cell DNA autoanalyzer mentioned in the previous article, but also consumables such as cell fixatives, dyes, and disposable cervical brushes. There are also many companies that do toC gene sequencing services, the profit point is mainly in the body fluid collector. Of course, doing hardware is relatively heavy for the company, and it does not meet the needs of each area. Therefore, it needs to be careful.
In general, the author believes that 2018 will be the starting point for the commercialization of medical AI enterprises. The certification progress of CNDA is also accelerating. There are already several companies that have obtained Type II certificates. Enterprises that have applied for Type III certificates have also been dusting for the past year or two. Settled. We are very optimistic about the tremendous improvement brought by artificial intelligence in the next five to ten years for disease diagnosis and treatment. We also hope that we can enjoy the health and quality of life brought about by technological advancement.
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