The North American Radiological Society Annual Meeting (RSNA 2018) opened in Chicago on November 25th. It is the world's largest radiological event, attracting tens of thousands of experts, scholars, and business representatives from more than 130 countries around the world. meeting. As a medical imaging AI company dedicated to the global market, Huiyi Huiying brought together two full-cycle AI products - "Breast Cancer Artificial Intelligence Full Cycle Health Management Platform" and "AORTIST2.0 Aortic Artificial Intelligence Cloud Platform" North American Radiation Annual Meeting, this is the first time that China's AI+ medical full-cycle products have gone to sea. Unlike other AI products, these two products extend the AI ​​from pre-diagnosis to treatment decision-making and prognosis prediction, enabling smarter adjuvant treatment decisions and more accurate prognosis predictions.
Patient-centered AI medical full-cycle products are favored
"Breast cancer artificial intelligence full-cycle health management platform" and "AORTIST2.0 aortic artificial intelligence cloud platform" had previously made their debut at the 2018 Chinese Radiology Academic Conference. This is the first time that China's AI medical full-cycle products have appeared on the international stage. The AORTIST2.0 Aortic Artificial Intelligence Cloud Platform is a smart stent placement solution for the B-type aortic dissection. It was jointly released by the Chinese People's Liberation Army General Hospital and Huihui Huiying in Beijing, China, in April this year, according to the Chinese People's Liberation Army. Guo Wei, director of the Department of Vascular Surgery at the General Hospital, said that this is the first automatic segmentation method for B-type aortic dissection artificial intelligence developed worldwide. Another "Breast Cancer Artificial Intelligence Full Cycle Health Management Platform", jointly developed by Huiyi Huiying and Intel, is dedicated to providing breast cancer artificial intelligence full-cycle medical solutions, comprehensive data on mammography, nuclear magnetics, pathology, etc. As a result, combined with neoadjuvant chemotherapy, the clinical stage of the tumor was reduced, and the breast-conserving rate and the success rate of breast-conserving surgery were improved. In addition, combined with imaging omics, accurate prognosis prediction and personalized follow-up programs.
Compared with medical imaging AI1.0 products that are only for disease screening and assisted diagnosis, AI medical full-cycle products have three distinct features: multimodal data based on CT, MRI, and clinical data, pathological data, genetic data, etc. Multi-dimensional data fusion, constructing a full image data link to achieve data fusion; by recommending personalized treatment decision-making programs, optimizing prognosis, etc., allowing artificial intelligence to create greater value for doctors and patients, achieving value integration; imaging AI products and doctors The medical treatment process is seamlessly integrated to help doctors improve diagnostic efficiency and achieve scene fusion.
Director of the Chinese Medical Association Radiology Branch, Chairman of the China Medical Imaging AI Industry and Research Innovation Alliance, and Director of the Department of Imaging Medicine and Nuclear Medicine of Shanghai Changzheng Hospital, Liu Shiyuan once said: The products and concepts of Huiyi Huiying AI full cycle health management very good. At present, the image AI has the following problems. The AI ​​light solves the single point problem and cannot solve all the problems of the medical image; the problem of solving the image by light cannot solve all the problems of medicine. Therefore, only the full-chain image AI, the full-chain medical AI solution can finally solve the doctor's problem and solve the patient's problem.
“Some of the operations performed in many top hospitals are restorative surgery, and the proportion of postoperative recurrence is high. This type of complicated disease, discharge is not the end point, prognosis prediction and discharge follow-up is a new starting point for medical treatment. Our goal is to design one The patient-centered product covers the patient's full medical cycle. In addition to improving the surgeon's surgical accuracy, the AORTIST system also integrates the imaging ensemble cloud platform we developed, embedding the prognostic prediction model, and whether the B-type dissection patient is postoperatively Adverse events will occur for prediction," said Chai Xiangfei, founder and CEO of Huiyi Huiying.
Full data platform drives AI into the medical cycle
Since 1898, in the field of medical imaging, we have experienced the era of physical drive represented by X-ray, ultrasound, nuclear magnetic, etc., and the application-driven era represented by image guidance and treatment planning. After 2010, we stepped in. In the era of data-driven smart medical care, its typical feature is to mine effective information in massive data and optimize diagnosis and treatment methods.
In the era of data-driven smart healthcare, data is the key. In the medical field, medical big data is special, medical big data is not “bigâ€, even image data is very limited, especially to single diseases, each person can not shoot a film every year, such as interstitial Pneumonia or a fracture of a certain part of the country may only have tens of thousands of patients per year, and it is also scattered in various hospitals. Data acquisition is very difficult. In addition, the data collection standards between hospitals are not uniform, and there is a large amount of unstructured data.
The development of AI depends on the development of data. The data center that combines image data, clinical data, test data, pathological data, and even genetic data will become the password for opening patients' health. At present, Huiyi Huiying has built a patient-centric full-scale data platform to provide a full-stack medical AI solution from diagnosis and treatment to research. The full process coverage of artificial intelligence is realized on a single disease, including intelligent screening to intelligent decision-making and prognosis to predict the whole disease cycle. Combined with the patient's full data, doctors can be effectively assisted in many aspects of the diagnosis and treatment, such as tumor staging, adaptive radiotherapy, prognosis follow-up.
Dr. Xing Lei, the chief scientist of Huiyi Huiying and a lifelong professor at Stanford University, said: “AI has a greater value in medical care and needs to build a multidimensional database. Using AI to integrate multidimensional data such as imaging, genetics, pathology and clinical The patient provides a personalized medical plan, recommends a surgical plan for the clinician, and provides medication guidance. The use of AI can provide patients with reasonable examination, treatment, follow-up and rehabilitation programs, provide full-process intelligent monitoring and management, optimize the diagnosis and treatment process, and save medical expenses. ""
“Our big data intelligent analysis system based on image omics brings together different topics from more than 500 hospitals. Currently, we have a large number of fine-labeled image data, and combined with massive clinical data, test data and pathological data, we have built a full-scale data center. It is the basis for realizing full-cycle artificial intelligence medical treatment." Chai Xiangfei said.
Huiyi Huiying's patient-centered, data-driven product logic coincides with the spirit of the RSNA conference. Vijay M., Chairman of the 2018 RSNA Conference Rao said in a keynote speech entitled "How Emerging Technology Will Empower Tomorrow's Radiologists to Provide Better Patient Care." The benefits of future AI and machine learning methods for radiology will be enormous, and these technologies will allow radiologists to work. More efficient, allowing more time to be transferred to the care of the patient. Technological innovation will move imaging technology toward "faster, safer, quantitative, precise and affordable" (faster, safer, quantitative, precise and affordable). Information centers established with important imaging data, clinical information, genetic characteristics, risk factors, etc. will greatly help patients with individualized treatment.
In addition, Huiyi Huiying launched a new product at RSNA: Intelligent Screening for Tuberculosis, which can be screened on X-rays to detect whether patients have tuberculosis, and to quantify the location of tuberculosis texture by CT analysis. Characteristics such as morphology, diagnosis, risk assessment, and treatment decision-making for doctors.
Previously, Huiyi Huiying signed a commercial cooperation agreement with Nuance, the world's largest professional R&D and sales company for speech recognition software, and the back supporter of Apple Siri's voice recognition technology. The four mature AI algorithms of Huihui Huiying have been successful. The AI ​​algorithm store platform of Nuance was logged in. During the RSNA meeting, the two parties jointly participated in the cooperation results.
At present, Huiyi Huiying AI products include partial screening products such as lung nodule screening, fracture screening, chest X-ray screening and tuberculosis screening, as well as two full-cycle products, which have already reached more than 800 hospitals of different scales in China. It has applications in more than 50 hospitals in Japan, the United States, Kazakhstan, and Brazil.
Guo Na, co-founder and chief operating officer of Huiyi Huiying, said: “Multi-modal image data application, full-cycle coverage of disease diagnosis and treatment is the development trend of the industry, and more diseases will realize the full-process AI application in the future. As a doctor's normal work, the computer will become the doctor's most effective assistant."
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