Thanks for visiting Imaging and Machine Vision Europe.

You're trying to access an editorial feature that is only available to logged in, registered users of Imaging and Machine Vision Europe. Registering is completely free, so why not sign up with us?

By registering, as well as being able to browse all content on the site without further interruption, you'll also have the option to receive our magazine (multiple times a year) and our email newsletters.

AI aids doctors assess Covid-19 CT scans

Share this on social media:

Infervision's InferRead AI system. Credit: Infervision

Artificial intelligence is helping doctors assess CT scans of the lungs of patients infected with the Covid-19 virus.

Infervision's coronavirus AI system, co-developed by Wuhan Tongji Hospital in China, is one such software platform, which has now been installed at the Campus Bio-Medico University Hospital in Rome to screen and diagnose Covid-19 patients.

Tongji Hospital in Wuhan, China was one of the first health centres in the world to equip itself with radiological AI solutions from Infervision in 2015, and asked the company for support when the latest crisis broke out three months ago.

The AI models are trained on CT images of the lungs of Covid-19 cases in Wuhan. The model is trained to detect possible coronavirus lesions in the CT scan, and to measure their volume, shape and density. It can also compare changes of multiple lung lesions, all of which helps the doctor make quick decisions.

Infervision's AI system can analyse a CT scan in 50 seconds.

Artificial intelligence can help to reduce the workload of doctors and streamline the way care is provided. It can provide an immediate diagnosis by automatically measuring and quantifying the density, morphology and volume of lesions. In addition, the automatic comparison of lesions helps doctors to monitor the patient's condition and development, while at the same time evaluating the effectiveness of treatments.

Elsewhere, a recent paper published in RSNA Radiology concluded that a deep learning model can accurately detect Covid-19 and differentiate it from pneumonia and other lung diseases.

The multi-centre study developed a neural network able to extract visual features from volumetric chest CT scans to detect Covid-19.

The model was also fed images of community acquired pneumonia (CAP) and other non-pneumonia CT scans, with the results showing that the neural network could distinguish between the different diseases.

Other work

In a blog for Mathworks, Barath Narayanan, University of Dayton Research Institute, wrote about applying a deep Learning-based technique for detecting Covid-19 on chest radiographs using Matlab.

Intel blog post: Huiying Medical: helping combat Covid-19 with AI technology

Related news

Credit: Centers for Disease Control and Prevention

02 April 2020

The 3D imaging system is able to inspect at a rate of up to 45,000 flatbreads per hour. Credit: Scorpion Vision

31 March 2020

Recent News

06 April 2020

Zensors' algorithms analyse feeds from CCTV cameras to provide real-time data on the number of people in an area and whether safe distances are maintained between them

02 April 2020

Research at MIT and Toho University in Japan, using high-speed imaging, has shown how aerosols from a sneeze and from speech can travel in the air

19 February 2020

Sony and Prophesee have developed a stacked event-based vision sensor, it was announced at the International Solid-State Circuits Conference in San Francisco

17 January 2020

The lens-free technology from CEA-Leti, which digitally reconstructs microscope images and will be presented at SPIE Photonics West, opens up fast, automated cell screening