A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan.
Deep learning, a subset of machine learning, draws on artificial neural networks that are designed to imitate how humans think and learn. We report the use in disease diagnostic, e.g. in imaging, research and its application in technologies such as robotics or wearables.
A new approach to tackling the spread of malaria in sub-Saharan Africa, which combines affordable, easy-to-administer blood tests with machine learning and unbreakable encryption, has generated encouraging early results in Uganda.
Researchers have developed a rapid and cost-effective particle agglutination based sensor that is powered by holographic imaging and deep learning
AI tools models are a powerful tool in cancer treatment. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions.
Scientists have created a deep learning method, RoseTTAFold, to provide access to highly accurate protein structure prediction.
Students at TU Eindhoven developed the world's first interactive drone that can transmit emotions.
A team of engineers from Rensselaer Polytechnic Institute and clinicians from Massachusetts General Hospital developed a deep learning algorithm that can help assess a patient's risk of cardiovascular disease with the same low-dose computerized tomography (CT) scan used to screen for lung cancer.
Using AI and mobile digital microscopy, researchers hope to create screening tools that can detect precursors to cervical cancer in women in resource-limited settings.
AI is helping researchers decipher images from a new holographic microscopy technique needed to investigate a key process in cancer immunotherapy “live” as it takes place.
Deep learning-based system enables dermatologist-level identification of suspicious skin lesions from smartphone photos, allowing better screening.