news, journals and articles from all over the world.
UT Southwestern researchers have developed a software tool that uses artificial intelligence to recognize cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.
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Researchers at University of California San Diego School of Medicine, as well as a diverse team of cardiologists and physicists, developed a machine learning algorithm to predict the life expectancy in heart failure patients.
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At the AVS 66th International Symposium and Exhibition, Oct. 20-25, Daniel Gunlycke will present a study on using symmetry to reduce the effects of random quantum entanglement in quantum computing applications. When deliberate, quantum entanglement can make algorithms more powerful and efficient, but uncontrolled entanglement adds unnecessary additional complexity to quantum computing, making algorithms suboptimal and more prone to error. Gunlycke says by reducing the frequency of accidental entanglements, quantum computing can be improved.
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Roswell Park’s Dr. Clare Twist led an effort to develop and validate a new treatment algorithm for infants and children with neuroblastoma. In a new study in the Journal of Clinical Oncology, the team reports that many patients can safely receive less extensive therapy.
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Machine learning could lead to automated processes that would allow soybean producers to diagnose crop stresses more efficiently. A multi-disciplinary team at Iowa State University recently received a grant to develop the technology, which could lead to unmanned aerial vehicles surveying fields and automatically analyzing crop images.
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Study Introduces Machine Learning as New Tactic in Assessing Cognitive Brain Health and Patient Care Worldwide, about 44 million people
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