In the matter of treating lung cancer, it is assumed that technology is more “real” than any enterprise.
At present, lung cancer is still the most common and deadly among all cancers in worldwide. The data shows that about 1.8 million new cases of lung cancer worldwide each year, accounting for 13% of all new cancer cases worldwide; about 1.6 million per year People die of lung cancer, accounting for 20% of global cancer deaths; lung cancer patients have a 5-year survival rate of less than 15%. The incidence and mortality rate of lung cancer in China has always ranked first, accounting for more than one third of the world’s lung cancer cases.
Because of the occultity of early symptoms of lung cancer, most patients have been locally advanced or have distant metastases at the time of diagnosis, missing the best treatment opportunity. If lung cancer can be detected at an early stage and appropriate interventions are used to standardize treatment, the patient’s five-year survival rate can be increased to 80%.
The development of precision medicine and artificial intelligence has provided an important breakthrough for the diagnosis of early lung cancer. At present, the auxiliary diagnosis of AI medical imaging products is the most significant, or to some extent, AI imaging is already the world. The largest auxiliary diagnostic system; but in the treatment of lung cancer, Imagine technology (hereinafter referred to as: Imagine) is more “real” than any enterprise .
To achieve integration of lung cancer diagnosis and treatment
Like most AI medical companies, the idea established in 2015 was initially based on lung AI assisted screening products, and gradually extended other products based on the principle of high frequency, just needed, and technology achievable; different Yes, in the field of “conquering” lung cancer, it can be said that it is spared, including product iteration, operational investment and scientific research investment.
From the product level, in 2015, I thought that I would first try to use AI technology to automatically identify lung nodule images. Nowadays, I am the first to release the “Lung Cancer Full Cycle Smart Solution”, trying to build Assisted diagnosis and decision-making system for the whole life cycle of artificial intelligence “prevention, diagnosis, treatment and management”.
What is the meaning of the “full cycle”? Think of the founderAnd CEO Chen Kuan explained, “In a real clinical setting, doctors need to know whether a nodule is lung cancer or not, whether it is benign or malignant; and if it is benign, how long it takes to follow up, if Malignant, when is the need for surgery; China has no clear diagnosis and treatment plan for small pulmonary nodules, how to standardize treatment; if surgery, open or minimally invasive, lobectomy or segmentectomy, etc.”
In his opinion, it is necessary to grasp every angle in the treatment process and string together the various links, including screening to diagnosis, and follow-up, observation, surgery, and healing. Patients can feel the value of AI. Therefore, the above-mentioned full-cycle solution scheme currently involves multiple levels, as follows:
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Promoting the rapid development of artificial intelligence in the whole cycle of lung cancer screening, diagnosis, treatment decision-making, prognosis, etc. through the scientific research quality control platform to help doctors AI scientific research innovation and clinical application;
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To achieve data unification and standardization management and improve data availability and security by building “image database” and “clinical database” data centers for medical institutions;
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Provides a full-cycle, standardized lung cancer solution for AI prevention, diagnosis, and treatment through the AI Medical Application Platform.
In addition to the value of early screening and early diagnosis and improving the efficiency of doctors’ work and reducing the rate of missed diagnosis and misdiagnosis, the integration of diagnosis and treatment can also bring about the development of hospitals and help the basic hospitals to improve the level of diagnosis and treatment. Liu Jinglian, dean of the Third Affiliated Hospital of Jinzhou Medical University, pointed out that the establishment of the hospital MDT team can fully communicate and communicate with patients through the intervention of AI, gain the trust of patients and achieve performance. Upgrade.
Of course, the landing of full-cycle products is not a one-off. Chen Kuan pointed out that “ diagnosis and treatment integration” is now an industry consensus, including equipment giants such as Siemens and GE are also on their traditional images. The analysis algorithm is upgraded to “AI” to provide a one-stop solution for the hospital.
But the serialization of the treatment process and the opening of the department are reflected in the overall data – especially the improvement of indicators of the level of the dean, such as the discovery and treatment of early lung cancer, but it is not easy. “They all take a lot of time to re-run and internal precipitation, and thisIt is the most valuable thing that I have accumulated over the past two years.
It is reported that At present, it is estimated that there are more than a dozen hospitals affiliated to the Third Affiliated Hospital of Jinzhou Medical University in Liaoning and the Second People’s Hospital of Liaocheng City in Shandong (prefecture-level hospitals and county-level hospitals) The main) jointly verified the feasibility of this product.
The time from the top three to the grassroots is mature
We know that public hospitals are the absolute core of the medical imaging industry chain. Whether it is algorithm optimization, departmental operation services, or diagnostic services, it is inseparable from the deep binding of public hospitals, even if it is The AI medical image is in a state of “trial”, and the top three hospitals have already become the “sweet” of various suppliers.
Taking the Zhongshan Hospital affiliated to Dalian University as an example, Deputy Director of Radiology Zhang Qing revealed that there were 3-4 AI medical treatments at one time. The company’s products are stationed in the hospital. However, Zhang Qing pointed out that has developed to the present, and only one of the doctors can continue to use it, “it”< Span> Let us rest assured, do not miss the diagnosis, improve efficiency, and then it is not so tired to do it.” As Chen Kuan said, AI medical products should be truly recognized, must be maintaining good stability in an open clinical environment, and Really stable to help doctors and patients.
Today, it is envisaged that there are more than 300 cooperative hospitals in the world, involving 8 different countries, and the average daily use of artificial intelligence to assist in the diagnosis of more than 40,000 patients, a total of 7 million medical records have been artificial Intelligent auxiliary diagnosis. As the product features improve, the promotion from the top hospital to the grassroots will follow. At this stage, it is assumed that in addition to the deployment of tertiary hospitals, it also penetrates into secondary hospitals. The “Lung Cancer Total Cycle Solution” mentioned above is a typical landing product.
At present, the grassroots is still the most typical and most prominent point of contradiction in medical resources. According to statistics, the referrals caused by the lack of medical imaging examinations in primary health care institutions account for 12.25% of the total number of referrals.
Chen Kuan believes that in the long run, thanks to the advancement of graded diagnosis and treatment, and the urgent need to improve the level of medical imaging services, the grassroots will be the future.