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Expert Comments

Zhengyu Jin

President, Chinese Society of Radiology Director
Radiology Department of Peking Union Medical College Hospital

"Evolution & Transformation"

It was about two years ago when I encountered Infervision. This group of very young scientists impressed me. Their development in China’s market is very promising. During our cooperation, young doctors and scientists from our hospital fully integrated into one with Infervision. We hope that through our joint effort, we will achieve excellence in the next few years.

Shiyuan Liu

President-elect, Chinese Society of Radiology
Director, Radiology Department of Shanghai Changzheng Hospital

"A.I. Application"

Infervision’s products are already online and have achieved a good performance. Regarding the outlook of deep learning, in general, that is only applicable to a product that has not been realized. In fact, the image analysis product based on deep learning is already landed, and artificial intelligence in now in progress at Shanghai Changzheng Hospital.

Liming Xia

Member of the National Society of MRI
Director of Radiology Department of Wuhan Tongji Hospital

"A.I. Significance"

Through cooperation with Infervision, I realized that the significance of artificial intelligence includes the following: First, to improve the efficiency of doctors, departments and hospitals, from the current product perspective, the computer analyzes pictures much faster than people, and therefore helps our patients…

Xiaoxiang Zhang

China Health Information Association Branch Leader
Director of Wuhan Tongji Hospital Information Center

"A.I. Supporting Healthcare"

Wuhan Tongji Hospital has several hospital areas, and we have established a set of information management platforms with 300 heterogeneous sub-systems. We indeed need artificial intelligence to provide us support. We now have so many hospital areas, and the total number of…

Highlighted Research

Computer Aided Diagnosis of Coronary Artery Calcification (CAC) with Convolutional Neural Networks

Coronary artery calcification (CAC) is a typical marker of the coronary artery disease, which is one of the biggest causes of mortality in the U.S. This study evaluates the feasibility of using a deep convolutional neural network (DCNN) to automatically detect CAC in X-ray images. 1768 posteroanterior (PA) view chest X-Ray images from Sichuan Province Peoples Hospital, China were collected retrospectively. Each image is associated with a corresponding diagnostic report written by a trained radiologist (907 normal, 861 diagnosed with CAC)…

Smoking Status Discrimination by MRI Images Based on Deep Learning Method

In this study, we assessed the feasibility of using deep learning techniques to predict smoking status from brain MRI images. We collected head MRI 3D-T1WI images from 127 subjects (61 smokers and 66 non-smokers). There were 176 image slices for each subject. The subjects were aged between 23 and 45, and the smokers had at least 5 years of smoking experience. Twenty-five percent of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the rest of the data were used as the training set...

A Preliminary Examination of the Diagnostic Value of Deep Learning in Hip Osteoarthritis

Hip Osteoarthritis (OA) is a common disease among the middle-aged and elderly people. 8 Conventionally, hip OA is diagnosed by manually assessing X-ray images. This study took the 9 hip joint as the object of observation and explored the diagnostic value of deep learning in hip 10 osteoarthritis. A deep convolutional neural network (CNN) was trained and tested on 420 hip X- 11 ray images to automatically diagnose hip OA. This CNN model achieved a balance of high 12 sensitivity of 95.0% and high specificity of 90.7%, as well as an accuracy of…