Publications by authors named "M Brudno"

Article Synopsis
  • The study focuses on how well different AI models can predict COVID-19 vaccine responses in solid organ transplant (SOT) recipients, who are at higher risk due to weakened immune systems.
  • It examines various traditional and deep learning models, concluding that a new routed LSTM model outperformed others in accuracy for predicting antibody levels 12 months post-vaccination.
  • The research highlights critical factors like age and immunosuppression that affect vaccine responses, suggesting AI could help customize vaccination strategies for vulnerable populations.
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With recent advancements in deep learning (DL) techniques, the use of artificial intelligence (AI) has become increasingly prevalent in all fields. Currently valued at 9.01 billion USD, it is a rapidly growing market, projected to increase by 40% per annum.

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Background: Electronic patient-reported outcome measures (ePROMs) are standardized digital instruments integrated into clinical care to collect subjective data regarding patients' health-related quality of life, functional status, and symptoms. In documenting patient-reported progress, ePROMs can guide treatment decisions and encourage measurement-based care practices. Voxe is a pediatric and user-centered ePROM platform for patients with chronic health conditions.

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Article Synopsis
  • Genetic testing demand is rising, leading to long waitlists and pressure on traditional genetic healthcare, highlighting the need for alternative solutions like e-health tools.* -
  • This study evaluates the Genetics Navigator, a digital platform designed to enhance genetic testing support by integrating with usual care provided by clinicians in both adult and pediatric contexts.* -
  • The effectiveness will be assessed through a randomized controlled trial measuring various outcomes, including participant distress, knowledge, and satisfaction, while considering cost-effectiveness compared to standard care.*
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Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites.

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