We’ve developed an artificial intelligence system to automatically detect seizures from long-term EEG recordings in an epilepsy monitoring unit. Strong performance in early datasets hints at big potential implications for epilepsy diagnosis and care.
Progress toward use of machine learning and deep learning techniques to inform epilepsy surgery decisions for individual patients is well underway. We share a status report on Cleveland Clinic’s experience in this space.
Differentiating true glioblastoma progression from pseudoprogression is a vexing challenge with big implications for management. New research reveals the promise of taking an algorithmic approach to MRI evaluation.
A novel biomarker derived via machine learning analysis of perivascular fat predicts cardiac risk better than current risk stratification methods, a new study finds.
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Machine learning can be applied to osteoarthritis gait models and joint-specific imaging analysis to identify opportunities for optimization across the care continuum.
A proprietary remote patient monitoring system—with smart knee sleeve—motivates patients while collecting outcomes. Dr. Brendan Patterson explains the recent feasibility study and how it might help establish the value of total knee arthroplasty.
Machine learning algorithms are one way we can approach the converging scarcity of time and abundance of patients in need of cognitive screening.
The shared grant to Cleveland Clinic and Case Western Reserve University will support application of computer post-processing to MR fingerprinting images to pinpoint seizure origins in challenging cases.
Cardiothoracic surgery has benefited from leading-edge data science methods more than almost any other specialty. We look back and forward with one of the giants of the field, Dr. Eugene Blackstone.
All but two of the variables most predictive of who’s likely to die on the heart transplant waitlist are included in the allocation system for donor hearts, finds an analysis of the SRTR national database.