Artificial Intelligence—Integrated Research Facility at Fort Detrick

Artificial intelligence (AI), in its many forms, is applied to infectious disease research at the IRF-Frederick. Primarily focused on medical imaging of preclinical models, state of the art methods are developed, applied to ongoing research and translated to human studies of disease.

In a collaborative effort with the Center for Infectious Disease Imaging (CIDI) at the NIH Clinical Center, post-doctoral and post-baccalaureate fellows develop novel machine-learning methods (such as, deep-learning-based medical image segmentation and disease state classification) toward the study of infectious diseases. The fellows are mentored by imaging scientists from the IRF-Frederick and radiologists from CIDI.

The AI Team works closely with the IRF-Frederick Imaging Sciences team and Chief Medical Officer to obtain multi-modality longitudinal medical imaging data and focus efforts on the most clinically relevant needs.

Main Areas of Focus

  • Application of machine-learning methods for predictive analyses of infectious disease state and correlation with non-imaging biomarkers
  • Segmentation of whole organs and abnormalities seen on multi-modal medical images
  • Radiomic feature extraction from segmented images and application to machine-learning classification algorithms
  • Use of image segmentation results from structural imaging applied to functional imaging (e.g., computed tomography [CT] to positron emission tomography [PET])
  • Automated quantification of medical images for use as endpoints in infectious disease imaging research studies

Capabilities

  • Automated pipeline for deep-learning-based image segmentation
  • Integration with high-performance computing environment (Locus), containing graphics processing unit (GPU) nodes for training and parallel execution of AI methods
  • Multi-organ segmentation (such as, liver, spleen, lungs, lung lobes, and lung lesions)
  • Creation of ground-truth annotations
  • Continuous improvements to deep-learning-based segmentation models
  • Collaboration with other IRF-Frederick teams (including Pathology and Histology, Immunology, and data management) to integrate multi-parameter estimations into machine-learning methodologies
  • Generalize specific segmentation models for use by external collaborators through the development of software tools for image data augmentation
A 3D image of lungs showing green areas of infiltrate.
Three-dimensional rendering of deep-learning-based segmentation of lungs and lung infiltrates (green), caused by viral infection.
Credit
NIAID IRF-Frederick

Three-dimensional rendering of deep-learning-based segmentation of lungs and lung infiltrates (green), caused by viral infection.

Credit: NIAID IRF-Frederick
CT and Pet images of non-human primate side by side
Automated deep-learning-based segmentation of liver (green outline) and spleen (pink outline) in computed tomography (CT) on the left and positron emission tomography (PET) on the right of a nonhuman primate.
Credit
NIAID IRF-Frederick

Automated deep-learning-based segmentation of liver (green outline) and spleen (pink outline) in computed tomography (CT) on the left and positron emission tomography (PET) on the right of a nonhuman primate.

Credit: NIAID IRF-Frederick
a series of 6 images side by side
Radiomic feature extraction (1st order, texture, and morphology) from segmented lungs on computed tomography (CT) image and correlation matrix of features.
Credit
NIAID IRF-Frederick

Radiomic feature extraction (1st order, texture, and morphology) from segmented lungs on computed tomography (CT) image and correlation matrix of features.

Credit: NIAID IRF-Frederick

Location

Integrated Research Facility at Fort Detrick (IRF-Frederick)


Contact Information

Jeffrey Solomon, Ph.D.
Senior Imaging Scientist (Contractor)

IRF-Frederick

Standards

All procedures are well-documented and adhere to standard operating procedures (SOPs), methods, or study-approved plans and agreements.

Collaboration Opportunities

  • Studies relevant to human disease
  • Use of surrogate systems to test clinical hypotheses
  • Use of biological systems to answer questions regarding disease pathogenesis and strategies for intervention including antimicrobials, vaccines, and other countermeasures
  • Developing and incorporating cutting-edge technologies to understand infectious diseases

Read more about how to work with the IRF-Frederick.