Students at Cranfield University have designed computer models that can identify COVID-19 in X-rays.
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Researchers have used machine learning to help reconstruct three-dimensional micro-CT images of fibrous materials.
Researchers developed a wearable X-ray detector prepared from nontoxic metal-organic frameworks layered between flexible plastic and gold electrodes for high-sensitivity sensing and imaging.
Researchers have found that out of the more than 300 COVID-19 machine learning models are not suitable for detecting or diagnosing COVID-19 from standard medical imaging.
Researchers have adapted a new class of materials for their groundbreaking volumetric 3D printing method that produces objects nearly instantly, greatly expanding the range of material properties achievable with the technique.
Researchers have developed a new model that accurately and automatically shows the exact location of mandibular canals.
Researchers have developed a computer model which predicts the neuronal activation patterns that the cochlear implant creates in the auditory nerve fibers.
An algorithm did better than experts radiologists at finding tiny brain hemorrhages in head scans — an advance that one day may help doctors treat patients with strokes.
Based on a convolutional neural network the tool is able to provide results within seconds, thus supporting the doctor with comprehensive image analysis.
Researchers announce critical advances in the use of 3D-printed coronary phantoms with diagnostic software, further developing a non-invasive diagnostic method for Coronary Artery Disease risk assessment.
Doctors could get a head start treating cancer thanks to new AI developed at the University of Surrey that is able to predict symptoms and their severity throughout the course of a patient’s treatment.
Researchers have developed a new technique of external ventricular drain insertion that involves the use of a mixed-reality holographic computer headset.