Algorithms help doctors make the best possible diagnosis
“This is the first time computers have interpreted heart scans to accurately predict how long patients will live. It could transform the way doctors treat heart patients,” said Dr Declan O’Regan, Research Lead from the Imperial College London MRC London Institute of Medical Sciences (LMS).
3D heart model
Artificial intelligence () can help doctors identify which patients with pulmonary hypertension are at greatest risk of deterioration based on analysis of virtual 3D hearts, a new study published in the Radiology journal yesterday revealed. Computer scientists from Imperial College London and clinicians from the MRC London Institute of Medical Sciences (LMS) have exemplified how could impact the future of medical technology by developing a software that can analyse images taken during an MRI scan and use them to construct a smart 3D heart that predicts patient survival rates. “It could transform the way doctors treat heart patients,” said Dr Declan O’Regan. “The computer is up to 80% accurate at predicting survival at one year. A doctor equipped with this new cardiac imaging approach would therefore be able to make more informed judgements about outcome than if they were relying only on current ways to investigate patient data,” he added.
Historic patient data linked to MRI
Data from 256 patients referred to the National Pulmonary Hypertension Service at the Imperial College Healthcare NHS Trust between May 2004 and October 2013 was included into the software in order to make detailed predictions. “Computational modeling provides a platform for improving or understanding of the heart, and the integration of experimental and clinical data is now bringing computational models closer to use in routine clinical practice,” states the study.
Inclusion of all available patient data
“The computer performs the analysis in seconds and simultaneously interprets data from imaging, blood tests and other investigations without any human intervention. It could help doctors to give the right treatments to the right patients, at the right time,” said Tim Dawes, another researcher involved in the developing team […]