CT "Alpha Go" has already been clinically implemented for a year

Major media in Wuhan: “It usually takes a physician 10 to 30 minutes to read a CT scan, while Infervision AI only takes 5 seconds. Human eyes can easily miss lesions less than 3mm, but AI can accurately detect them and assists physicians for the optimal diagnosis of each patient. Today, "Discussion between Infervision and Wuhan Tongji Hospital" seminar is held in Wuhan city, and many positive feedbacks were received given by physicians in Wuhan Tongji Hospital.

Physicians give Infervision's product a nickname: CT “Alpha Go.” It not only provides quick and accurate diagnostic results, but the product is also highly integrated with the PACS and worklist systems from which the physicians normally load and read images - the physicians can click on to the Infervision icon, where a window will pop up and show the AI results conveniently.

Infervision’s “CT Alpha Go” uses CNN machine learning model, which has already been trained with over 100,000 chest CT scans before it starts the ‘job’ at the hospital. Because of the massive volume of data it can study through the deep learning process, it became highly experienced, and the amount of “experiences” it gained far exceeds that of a human in such a short time.

Professor Liming Xia, director of radiology in Wuhan Tongji Hospital, said that CT Alpha Go’s ability to interpret medical images had reached the level of a human radiologist. It could detect lung nodules with a remarkable 90% accuracy rate while taking only 5 seconds. Besides, it is sensitive to details and has successfully reduced missed diagnosis in Tongji Hospital.

As a summary note, “CT Alpha Go” has been implemented at Tongji Hospital for over a year now and had assisted with over 100,000 image diagnostic cases. It is breaking the record of AI image screening in the medical AI industry.

Infervision’s InferRead CT Lung in Wake Radiology

Doctor’s Story - Beijing Friendship Hospital

Infervision’s InferRead CT Lung in Wake Radiology

Doctor’s Story - Beijing Friendship Hospital