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
  • computer-aided diagnosis
  • computer-aided screening
  • analyzing complex patterns and images
  • screening for diseases
  • natural-language processing and understanding
  • medical decision-making
  • predictive modeling
  • computer vision
  • robotic and image guided surgery
  • personalized imaging and treatment
  • drug discovery
  • radiomics
  • machine/deep learning-based segmentation, registration, etc.

Additional support

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
Grant Number Project Title Principal Investigator Institution
5-R21-EB024025-02 Deep radiomic colon cleansing for laxative-free CT colonography Janne Nappi Massachusetts General Hospital
5-R01-EB020683-04 Quantitative Image Modeling for Brain Tumor Analysis and Tracking Khan Iftekharuddin Old Dominion University
5-R21-EB026665-02 Developing Database and Software infrastructure for Quantitative Radiologic Analysis of Lumbar Radiculopathy Luke Macyszyn University of California Los Angeles
5-U01-EB023822-04 Software tool for routine, rapid, patient-specific CT organ dose estimation Taly Schmidt Marquette University
5-R01-EB006733-11 Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI Pew-Thian Yap Univ of North Carolina Chapel Hill
5-R01-EB025032-04 Predicting the early childhood outcomes of preterm brain shape abnormalities Natasha Lepore Children'S Hospital of Los Angeles
1-R21-EB029627-01 Machine learning for fast motion compensated quantitative abdominal DCE-MRI Sila Kurugol Boston Children'S Hospital
1-R21-EB029607-01A1 Decoding inner speech: An AI approach to transcribing thoughts via EEG & EMG Jose Cortes-Briones Yale University
1-R43-EB029863-01A1 A New Tool to Rapidly Diagnose Sepsis using Flow Imaging Microscopy and Machine Learning Christopher Calderon Ursa Analytics, Inc.
5-R01-EB029398-02 Synchronized brain dynamics and eye movement trajectory for objective evaluation of robot-assisted surgical skills Somayeh Besharat Shafiei Roswell Park Cancer Institute Corp