BioMind has launched an artificial intelligence (AI) application to assist in the detection of COVID-19 associated pneumonia on medical imaging.
The AI application identifies anomalies in the lungs and assists in detection of COVID-19 pneumonia. It analyses the lung pathology on chest CT to aid in the assessment and comes with triage capability. With the sudden surge in admission of respiratory-related patients globally, triaging can help to reduce the increasing burden on the intensive care units (ICU) and address the shortage of medical resources.
Raymond Moh, CEO of BioMind, said: “We are proud to receive the Health Sciences Authority’s approval for our COVID-19 diagnostics support product. This was born out of the immense efforts and collaborations between clinicians, lung specialists and our engineers. In an unprecedented crisis like this, we hope to quickly scale our solution in Asia, Europe, Middle East and USA to equip physicians with smart tools to detect and manage COVID-19 pneumonia. Artificial intelligence will be their useful assistant daily in this long battle.”
Hanalytics, a medical AI company behind this solution, is also the brainchild of the BioMind application to assist diagnosis in neurological disorders including brain tumours and haemorrhagic stroke.
The company uses a deep learning approach and develops a predictive application that is trained on a large volume of CT scans which are clinically confirmed to have COVID-19 infections. This allows them to be used as an assistive tool to physicians to recognise diseases on medical scans, automate quantitative analysis and assist in report writing quickly.
Hospitals around the world are currently using CT scans to confirm diagnosis and manage the COVID19 disease. BioMind (COVID-19) has been deployed in close to 150 hospitals for research. One such hospitals in Europe is Hôpitaux Robert Schumann (HRS) based in Luxembourg.
Dr. Niedercorn, chief radiologist at HRS, said: “In addition to the detection of COVID-19 lung damage, this algorithm will allow us to quantitatively and objectively assess the lung volumes affected and their evolutionary monitoring over time, this can positively impact on the better management of our patients.”