NCIMI to team with GE Healthcare to look into Algorithms for Covid-19 Diagnosis
The National Consortium of Intelligent Medical Imaging (NCIMI) has teamed up with GE Healthcare, led by a team based at the University of Oxford, in order to research effective algorithms aimed at predicting which Coronavirus patients will develop severe complications once infected as opposed to those which may emerge from the infection with less dire complications.
Currently, medical professionals cannot easily determine which patients with an active Coronavirus infection will require hospitilisation and ventilator support. It is presently not obvious which patients will suffer life-threatening complications, or indeed long-term consequences from Coronavirus-induced lung damage.
The consortium is striving to create algorithms incorporating data gathered from thousands of patients, laboratory and clinical observations along with medical imaging in order to produce a far more accurate and speedy diagnosis of which patients may be in danger of further complications versus those who are likely to have a speedy recovery.
“It would be extremely valuable to predict at a relatively early stage in the disease which patients will do well, which are at risk of imminent deterioration and should be admitted to ICU as they will need more intensive support, and which are at higher risk of delayed deterioration and need to be actively monitored.” Professor of radiology at the University of Oxford Fergus Gleeson stated recently. “These distinctions would allow hospital resources to be targeted to those that will need them whilst in hospital and following discharge.”
Presently, patients who appear stable after testing positive for Coronavirus can deteriorate rapidly, whilst others will show little to no symptoms comparatively. The incorporation of data derived from the consortium’s algorithms may help hospital staff more readily identify with greater ease the more urgent cases, taking out the guess-work and assigning limited resources to those who are likely to need more specialised treatment.
Yet another practical application of the ever-expanding usage and implementation of algorithms and amalgamated data.