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Analysis EO
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Analysis EO
Mar 5, 2020 11:41 am ·

Open Source, Open Mind: Baidu and LinkingMed Help Doctors in Medical Images

LinkingMed has launched its AI-based detection model for pneumonia CT-screening and prediction based on the Baidu’s PaddlePaddle (Parallel Distributed Deep Learning), EqualOcean learned from the medical imaging firm. A Hunan-based hospital affiliated to Xiangnan University became the first medical institution to use the system. This AI-equipped diagnosis system can detect and contour the lesion, picture the diagrams of the density of the two hemisphere lungs, and visualize a series of quantitative benchmarks such as quantity and volume percentage. All these tasks can be completed within less than one minute, with an accuracy rate of  92% - 97%, LinkingMed claims. So far, LinkingMed has not initiated any commercial plans for this AI-based pneumonia detection system. “The motivation to launch this AI medical diagnosis model is only to help fight against the spread of coronavirus,” Mr. Ryan Zhang, the CEO of LinkingMed, said. “We will unconditionally contribute our AI power to containing this plague, as much as we can,” he added. The high-tech medical solution provider focused on solving the compatibility issue with different types of scanning equipment by cooperating with research entities. At the development stage of the algorithm used by the system, the Xiangnan University-affiliated hospital provided professional clinical instructions regarding the data annotation, module design and set strict acceptance criteria to satisfy various medical conditions. This online AI screening system will be deployed at multiple hospitals in Hubei, Chengdu, and other severely-affected areas. Founded in 2016, LinkingMed is an AI technology enterprise in the field of oncological radiation therapy. The Beijing-based medical high-tech company sells techniques and cloud services related to the organic contour, target contour, and radiotherapy to medical institutions. By leveraging the internet and cloud platforms, it also provides remote collaboration and relevant website services for oncological doctors and physicists. During the past four years, LinkingMed has received d four funding rounds, including the recent one worth CNY 40 million in October 2019. Linear Venture is the serial investor for LinkingMed from Series Pre-A finance to the last Series A finance. Medical imaging is a niche business given lightweight and comparably less attention as Chinese medical device makers are promoting their ‘globally competitive’ strategy. Now this area is increasingly highlighted, as the novel coronavirus continues to spread and consumes a lot of medical facilities and doctors across China. The domestic high-tech medical device market rose in 2016, a fierce tussle participated by United Imaging (联影医疗), Huiyihuiying (汇医慧影) and VoxelCloud (体素科技). Most players use essentially indifferent AI technology while base the competency on different sources. These providers still need to consider some fundamental issues to apply AI in daily life. The first is developing the algorithm for the specific problem, for instance, coronavirus event. The other is high-quality data annotation, which takes time and hard work. Additionally, the lack of industry standards, quality protocols, and practice guidelines stand as the major problems ahead of the industry. For patients, time is life. CT imaging plays a critical role in identifying the infected patients, serving as a surrogate to PCR (polymerase chain reaction) diagnostic, according to a previous report. In the Huoshenshan (‘Fire God Mountain) hospital, one of the frontline hospitals, the CT lung screening by InferVision (推想科技) helped with the lab’s capacity as the number of suspected cases rose quickly.  To facilitate the detection process and guarantee the test results, LinkingMed incorporated the AI learning framework into the CT imaging process by utilizing Baidu’s PaddlePaddle. Injected with the AI’s deep-learning power, this open-source online diagnosis model can improve the efficiency and ease the pressure on the clinical doctors.  Before 2020, ‘Artificial Intelligence’ was a buzz word. Now, the outbreak of coronavirus is pushing many key industries to accelerate their AI processes, especially, in the urgent demand for medical facilities. The epidemic has forced Ali Health (阿里健康), Yitu Technology (依图科技), Huawei (华为), Deep Wise (深睿医疗), and other tech companies to introduce their AI-equipped diagnostics, in different aspects, aligning a joint wholistic force to ease the infection.  The PaddlePaddle of Baidu is an open-source deep-learning Chinese platform for industrial applications. Similar to Google’s TensorFlow, PaddlePaddle comprises core learning-framework, model bank, development tool kits, and online services supporting over 1.5 million developers in businesses.  The option to cooperate with PaddlePaddle also indicates LinkingMed’s motivation for its AI diagnostic system. “To curtail the current tension in Korea, Iran, and Japan, we want to provide technical support through Baidu’s platform for international medical institutions and developers,” says Mr. Zhang, “We also will keep an open mind to work with different medical entities, including online healthcare. The goal is to inject technology, whilst fulfilling our social responsibilities. ”

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Analysis EO
Jul 24, 2019 03:18 pm ·

Infervision Plans AI Tool for Whole Treatment Process of Lung Cancer

Infervision is planning to debut a medical imaging AI solution for the whole treatment process of lung cancer, including prediction, diagnosis, treatment and prognosis, according to Chen Kuan, Infervision’s founder and chief executive officer, disclosing it in a Chinese medical forum on July 20. The new solution has been adopted in two Chinses tertiary hospitals, verifying the feasibility of clinical practice, said Mr. Chen. While most of Chinese AI diagnosis products providers mainly focus on the diagnosis sector, their clients - hospitals - expect for an integrated system to help doctors make decisions in the whole workflow. In the past four years, the Beijing-based startup has employed deep learning algorithms to analyze medical images from DR, CT and MRI, developing AI diagnosis solutions for lung cancer, chest-related disease, stroke, bone disease and breast cancer. To meet the demand of doctors, the company also develops a medical research platform, which becomes the passport for Infervision to build cooperation with hospitals. At present, Infervision is operating in 32 Chinese cities, and another seven countries in regions of North America, Asia and Europe, cooperating with over 300 hospitals worldwide. Its intelligent medical solutions complete more than 38,000 diagnosis per day, according to Mr. Chen. The ten-year survival rate reaches 92% to date for the patients in stage 1 lung cancer - stage 1 lung cancer means that a tumor is discovered, but it is still localized within the lungs. However, only around 4.2% of the patients in stage 4 can be alive after five years since they start treatments. Thus, detecting lung cancer’s risks in the early stage is the key to the successful disease treatment, said Wang Chen, dean of Peking Union Medical College. Chinese government issued a document for the development of the nationwide medical and healthcare careers on July 15, setting a goal that the five-year survival rates of all types of cancer should be above 43.3% in 2022 and 46.6% in 2030. There’s a long way to go obviously. Lung cancer is the targeting disease for most of the Chinses AI diagnosis tools developers, because of the sufficient medical imaging records generated from Chinese lung cancer patients. Both the incidence rate and mortality rate of lung cancer rank the first among all types of cancers, 11.6% and 18.4% respectively in the world. In 2017, more than 100 companies tapped a huge need for lung cancer accurate medical diagnosis, due to a large number of patients in China and a shortage of well-trained doctors able to diagnosis accurately, according to Zou Guowen, partner of Dalton Venture, a Chinses medical-oriented VC institutes. Investors are optimistic about the potential use of AI to recognize cancer, with more than a dozen companies in China raising USD 142 million in venture capital and private equity funding in 2017 and 2018, according to KPMG.  Infervision is regarded as a dark horse in the AI medical area, attracting reputable investors to join the funding game, such as Sequoia Capital China, CDH Investments, Qiming Venture Partners. The total funding of the company in the past four years surpasses CNY 600 million (USD 87.18 million). However, the China Food and Drug Administration (CFDA) upgraded the Medical Device Classification Catalog last August, putting AI products for diagnosis into the list of Class III devices. The companies producing such products have to apply for the Class III device licenses, which can take more than two years to obtain. The AI diagnosis products can't be listed in the hospitals' procurement catalog without the license, resulting in obstacles of commercialization because selling products into hospitals is a mature business in the market. No company received a license to date, according to Cao Luqian, investment manager of Jiangmen, a fast-growing VC company led by the former chief executive officer of Microsoft Accelerator China. The regulatory process is also pricey, costing up to CNY 4 million (USD 581 thousand), said Zhong Xin, chief executive officer of 12 Sigma. “It takes more money and time to get a license and many small companies will find it hard to get their products into the market,” said Mr. Zhong, “It’s not easy for newcomers.”

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Analysis EO
May 13, 2019 12:05 am ·

Without CFDA Approval, How Business Run for AI Medical Imaging Diagnosis?

After introduced and explained by Geoffrey Hinton’s paper published in 2006, deep learning algorithm has been applied in improving the accuracy of image recognition by computer scientists these years. In the past of decade, scientists are continually making breakthrough on the image-related technologies, including image recognition, segmentation, character extraction, quantitative analysis and comparative analysis. These advanced technologies has been applied in various industries, especially in the works relying on images originally. Medical imaging diagnosis is a typical one. Medical image, including many types like X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), etc., is used to diagnose many diseases by doctors today. However, the number of Chinese imaging doctors is in short supply, and there is a shortage of doctors with abundant clinical experience, especially in primary hospitals in towns and villages. In addition, doctors diagnose mainly relying on their experience, so there are lots of misdiagnosis and missed diagnosis. Since 2012, many start-ups have emerged in China, which apply image recognition and other image-related techs in the process of image diagnosis. They hire top-notch artificial intelligence engineers and outstanding imaging doctors to train algorithmic models and develop medical image-assisted diagnostic products. These products can greatly improve diagnostic efficiency and diagnostic accuracy, which are really good assistants for doctors. However, these products are forbidden to commercialize without the certification from CFDA (China Food and Drug Administration). In other words, if require the certification, the products can enter the hospital’s medical suppliers list and can be purchased and reimbursed by national medical insurance fund. Because these AI Medical Imaging Diagnosis Products are newly emerging ones, CFDA does not have mature certification standards, so the assessment progress is really slow. For now, there are only two China’s companies requiring the certifications. So how do these companies run their business and where does income come from? Medical valuation of AI medical imaging diagnosis AI medical imaging diagnosis products emerged in China are mainly for cancers,especially lung cancer and breast cancer. There are a large number of cancer patients in China. More than 10,000 people were diagnosed with cancer every day in 2014, which means that 7 people were diagnosed with cancer every minute, of which lung cancer ranked first, according to the National Cancer Center. In 2017, the number of lung cancer patients reached 800,000, and the deaths were nearly 700,000. And the death units are still growing at a rate of 26.9% per year. It is estimated that there will be 1 million people dying from lung cancer in China in 2025, according to the Capital Medical University. Breast cancer is the most severe malignant tumor among female population. According to the cancer report published by the World Health Organization's International Agency, which contains the research results from 184 countries and regions, the incidence and mortality rate of breast cancer among Chinese women is at a low level in the world, but the morbidity and mortality have increased rapidly in the past 20 years. In addition, the number of Chinese breast cancer patients accounts for 11.19% of the world. According to the National Cancer Center, there were 278,900 new breast cancer patients emerged in 2014, accounting for 16.51% of the incidence of female malignant tumors, which ranks first in the incidence of female malignant tumors. So how many imaging doctors does China have? The answer is only 150 thousand. Besides, more than 50% of imaging doctors work more than 8 hours a day, and 20.6% of them work more than 10 hours a day. The annual growth rate of medical imaging data in China is about 30%, while the annual growth rate of imaging doctors is only 4.1%, according to VCBeat.net, a medical content platform. Under the heavy workload, doctors are probably to make mistake on diagnosis. DeepCare, a Chinese start-up developing related products, made an experiment to analyze the performance between doctors with different working length. In this experiment, a pathologist with 40-year working length and a pathologist with 10-year working length diagnosed the same group of lymphatic metastasis digital pathological sections. The result shows that the disparity of true positive rate (TPR) between these two doctors are as high as 30%. Using AI to help doctors make diagnosis can greatly reduce the workload of doctors and improve the accuracy. For example, InferVsion (推想科技), a China’s start-up developing AI-enabled medical imaging diagnosis solutions, provided its two products, AI-DR and AI-CT, to Tongji Medical College of HUST. These products detected lung cancer from 110,000 X-rays and more than 3,000 CTs, and the detection rate surpassed 92% and 95% respectively with only 5 seconds. Landscape of AI medical imaging diagnosis market So how many companies develops these kind of AI products? According to EO Company, there are 61 typical companies in China, and 41 are founded between 2014 and 2017. Since 2016, investments in China's AI medical enterprise has begun to explode. The number of investments has increased from 10 in 2015 to 22 in 2016, while the investment amount has increased from CNY 162 million (USD 23.75 million) in 2015 to CNY 1291 million (USD 189.25 million) in 2016. The peak of investments occurred in 2017, when there are 35 investment totally in CNY 4.95 billion (USD 0.73 billion). It is said by some investors that there are more than 100 companies developing this kind of products in China, but some companies are not known to the public. Among these 100 companies, 90% of them develop diagnostic products for lung cancer, because the number of cancer patients in China is huge, which means the lung cancer case data is rich. And the algorithm models need a large amount of data for training. Besides, there are abundant papers and academic materials to analyze lung cancer, and the lung cancer’s research is the most mature among various types of cancer. In addition to lung cancer, there are also some products for other diseases, such as diabetes, stroke, breast cancer, esophageal cancer, fractures, skin diseases, liver cancer, prostate cancer, and cardiovascular diseases. Representative companies in this field include inferVision, DeepWise (深睿医疗), 12Sigma (图玛深维), YITU Medical (依图医疗), VoxelCloud (体素科技), United Imaging Healthcare (联影医疗), Airdoc, etc. InferVision was founded in 2015, and it is the top enterprise in this field. It has raised for 5 rounds of financing totally in more than CNY 500 million (USD 73.30 miilion). Its core product namely InferRead CT Lung, which is extremely sensitive to tiny nodules and ground glass nodules. It can help doctors improve diagnostic efficiency by 30%-50% with ensuring accuracy. As of May in 2018, the product has been adopted in more than 150 China’s hospitals, mainly in the first-class hospitals. DeepWise was founded in 2017, a start-up with rapidly development. It has raised 3 rounds of financing within one year since it founded, totally in nearly CNY 300 million (USD 43.98 million). The company's core product is also for cancer diagnosis, and it only takes 30 seconds to make a diagnosis. The accuracy of this product reaches 98.8%, which means its sensitivity and specificity of lung nodule detection have reached the international leading level. As of December 2018, this product has been adopted in nearly 300 China’s hospitals. 12Sigma was founded in 2016, and it has raised 4 rounds of financing totally in about CNY 250 million (USD 26.65 million). Its core product namely is σ-Discover-Lung, which is also for the lung nodule detection and analysis. In addition to leveraged by deep learning algorithm, this product is also improved by the 3D segmentation technology independently developed by 12Sigma. As of June 2018, this product has been adopted to more than 100 China’s first-class hospitals. YITU Medical was founded in 2012, which is a subsidiary of YITU, a China's AI unicorn enterprise. Its core product, Care.Ai TM, is for a variety of diseases, including lung cancer, bone age detection, pediatric intelligent diagnosis, and medical record intelligent search engine. As of January 2019, more than 50 top hospitals in China has adopted this product. However, there are only two China’s companies obtaining the certification from CFDA. Without this certification, products cannot enter the hospital's procurement list. On August 1 of 2018, the new edition of the Catalogue of Medical Devices, which was updated by CFDA, was officially put into effect. Among them, the category of "in vitro diagnostic software" contains the AI medical imaging diagnosis products. According to this category, if the diagnostic software provides diagnostic recommendations through its algorithm with only has the auxiliary diagnostic function - does not directly give the diagnosis conclusion - the related products are managed as the Class II of medical devices. If the software automatically identifies the lesion by its algorithm and provides clear diagnostic prompts, the software’s risk is relatively high, and the related products are managed as the Class III of medical device. The third type of medical device application is more difficult than the second type, in which clinical trials are needed for the third type. Till now, only Landing-Med (兰丁医学) and EDDA Technology (医软信息科技) have obtained CFDA certification, so the two companies have already bid farewell to the free trial stage, and customers can pay them legally. So how do other companies that have not been certified run business? In fact, companies and investors are trying to explore a new business model. At present, these companies earn money by cooperating with doctors in scientific research. Enterprises provide doctors with support for algorithm models, computing sources, data processing and analysis. And companies can obtain a part of the compensation from research funding. Of course, another more important value of scientific research cooperation is to cooperate with hospitals and use the hospital case data to train their algorithm models. Most companies mentioned above are applying for a Class II certificate, but there is no new progress. According to industry investors, although the new edition of the Catalogue of Medical Devices has been in operation for more than half a year, the certification system for AI imaging diagnosis products is still being improved. It is expected that this kind of products will successfully apply for Class II certificates by 2020, and certificated products should be hardware combining with software.