A predictive algorithm has distinguished patients free of obstructive sleep apnea from those with three levels of the condition—mild, moderate and severe—with 91% accuracy. And it did so using only 3D photos of the subjects’ faces.
During the COVID-19 crisis, what’s a lung-screening program to do with a patient referred for surveillance chest CT after a pure ground glass nodule incidentally turned up in his or her lung?
Using brain MRI and a deep learning network, researchers have achieved 97% accuracy at classifying a gene mutation indicative of growth in localized gliomas.
COVID-19 can show up incidentally when a patient’s lungs are partially readable on CT scans of the abdomen or neck. And the findings can help identify patients who should be strictly quarantined.
A deep-learning algorithm has accurately measured 26 pairs of uneven leg lengths on children’s x-rays at a rate 96 times faster than that recorded by an experienced, subspecialty-trained pediatric radiologist using manual means.
Researchers in Northern Italy have found CT quantification can be used to predict how severe the disease will become in positive-testing patients whose lungs are relatively clear when they’re admitted.
A supplier of medical imaging products and services in West Michigan is going beyond applauding local healthcare workers on the front lines of the battle against COVID-19.
Johns Hopkins researchers have demonstrated the use of photoacoustic imaging to guide catheter-based cardiac interventions such as radiofrequency ablations used to correct arrhythmias.
