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Many patients with mild concussion may not realize the potential lifelong severe health issues if left untreated. A collaboration between USC Viterbi School of Engineering and USC Leonard Davis School of Gerontology has utilized machine learning to predict concussion status in patients. The research, led by Benjamin Hacker, has been published in the Journal of Neurotrauma.
Current clinical practice for concussion diagnosis often relies on basic cognitive tests, which may not be sensitive enough to detect milder cases. The team built a classifier model using diffusion-weighted imaging and machine learning, achieving 99% accuracy in both training and testing samples. This innovative approach could revolutionize concussion diagnosis and potentially disrupt the field in a positive way.
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Sam Altman today revealed that OpenAI will release an open weight artificial intelligence model in the coming months. "We are excited to release a powerful new open-weight language model with reasoning in the coming months," Altman wrote on X.
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