Recently, the medical quasi-intelligence announced the completion of 45 million yuan A round of financing, this round of financing led by Hanergy Venture Capital, multi-dimensional Haituo as financial advisor. Lv Chenxi, founder of Physician Intelligence, said that this round of financing will be mainly used to accelerate the development of AI imaging multi-disease new products, expand sales and channel partners, and further accelerate the commercialization of AI systems in hospitals.
According to public information, Beijing Medical Standard Intelligent Technology Co., Ltd. was established in November 2017. It is a company specializing in artificial intelligence-assisted medical imaging diagnosis. It is committed to promoting the breakthrough of artificial intelligence in medical imaging and enabling artificial intelligence to assist imaging diagnosis. Become an efficient and effective assistant to radiologists.
It is understood that lung cancer is the fastest growing cancer of morbidity and mortality. CT imaging based lung cancer screening is recognized as the most effective prevention and treatment method. Among them, malignant pulmonary nodules are one of the early manifestations of lung cancer. Medical quasi-intelligence has a solid algorithm and model accumulation for CT-based pulmonary nodule artificial intelligence detection. The auxiliary diagnostic system has nodule automatic detection function, nodule characteristic analysis function, and benign and malignant grading function. Especially at high detection rates, it is still possible to maintain a low false positive rate within 2 nodules per patient. Among them, the high detection rate of ground glass nodules is widely praised by several top users. At present, more than a dozen users have chosen to pay for smart detection services.
In the middle of this month, the medical quasi-intelligence will also release the “multi-stage lung nodule follow-up comparison function†which is widely expected by users. This function can automatically search the lung nodules CT slices of patients at different stages, and extract the same part nodules. Contrast, analysis, and finally generate lung nodule volume doubling curve and density curve, the product can help doctors monitor the morphological changes of nodules.
It is worth mentioning that the medical intelligence has also won the LUNA16 artificial intelligence lung nodule detection competition champion, and won the championship on the list for several months, and also as a core member with the team at the time to get the Google Kaggle Data Science Bowl lung cancer The fourth place in the competition and the winner of the Alibaba Tianchi Medical Pulmonary Nodule Test Contest Finals.
Similar to the solid lung nodule detection product, the mammography X-ray assisted diagnosis system developed by the medical quasi-intelligence has the functions of automatic location of lesion location, BI-RAIDS grade division, and benign and malignant discrimination. It can effectively assist radiologists in reading films, improve the efficiency of doctors' reading, and assist doctors to improve the accuracy of diagnosis.
In June 2018, the first mammography AI detection system of the medical quasi-intelligence was installed at Peking University Cancer Hospital, and then its AI products were installed at the Cancer Hospital of the Chinese Academy of Medical Sciences. So far, the medical mammography target AI detection system has installed dozens of users, and doctors have evaluated the product well.
At present, the medical intelligence has achieved full coverage of the breast, more than 93% of the lesion detection rate, BI-RADS classification, gland type analysis, calcification statistics, benign and malignant analysis, lesion length and diameter and area calculation and other functions Loved by doctors.
It is understood that medical quasi-intelligence has cooperated with a number of top three hospitals, and is committed to promoting the breakthrough of artificial intelligence in the field of medical imaging, making artificial intelligence-assisted imaging diagnosis an efficient and effective assistant for radiologists.
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