Artificial Intelligence, Machine Learning, and Deep Learning (NIBIB) is sponsored by National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH. Supports mission-aligned projects focused on the development of transformative machine intelligence-based systems, emerging tools, and modern technologies for diagnosing and recommending treatments for a range of diseases and health conditions.

This includes early-stage development of software, tools, and reusable convolutional neural networks.

Artificial Intelligence, Machine Learning, and Deep Learning | National Institute of Biomedical Imaging and Bioengineering Artificial Intelligence, Machine Learning, and Deep Learning Director, National Centers for Biomedical Imaging and Bioengineering Division of Health Informatics Technologies (Informatics) Program Area: Artificial Intelligence, Machine Learning, and Deep Learning Division of Health Informatics Technologies (Informatics) Program Area: Artificial Intelligence, Machine Learning, and Deep Learning Supports the design and development of artificial intelligence, machine learning, and deep learning to enhance analysis of complex medical images and data.

The emphasis is on development of transformative machine intelligence-based systems, emerging tools, and modern technologies for diagnosing and recommending treatments for a range of diseases and health conditions. Unsupervised and semi-supervised techniques and methodologies are of particular interest.

Program priorities and areas of interest: clinical decision support systems analyzing complex patterns and images natural-language processing and understanding robotic and image guided surgery personalized imaging and treatment machine/deep learning-based segmentation, registration, etc. This program also supports: early-stage development of software, tools, and reusable convolutional neural networks data reduction, denoising, improving performance (health-promoting apps), and deep-learning based direct image reconstruction approaches that facilitate interoperability among annotations used in image training databases NIH Demystifies Vital Biomedical Tech for Congressional Staff Biomedical engineers and imaging researchers gave 45 congressional staff a glimpse of transformative medical technologies on the horizon, underscoring the crucial role of medical tools in human health.

AI tool can track effectiveness of multiple sclerosis treatments A new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS) has been developed by University College London researchers.

Source: University College London News Researchers lend expertise to improve treatment for childhood brain cancers Brain cancer is the second most common cancer in children after leukemia, and it is also the deadliest, due to the fact that brain tumors are diverse, resistant to treatments and often hard to access surgically.

A collaborative team of researchers at several institutions have developed a new way to profile brain cancers in children, paving the way for improved diagnostics and treatments. Source: UTSA Today NIH announces finalists of endometriosis diagnostics competition The National Institutes of Health (NIH) has selected four finalists with innovative, non-invasive technologies that seek to improve diagnosis of endometriosis.

Portable MRI, enhanced by AI, proves viable in brain imaging for dementia The low image quality of small, affordable MRI machines have prevented their widespread use. But a boost from AI could close the gap, bringing MRI to more patients.