AI methods are developed and applied to medical imaging as part of infectious disease research at the IRF-Frederick. AI-guided preclinical models can be 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
- Segmentation of histopathology slides using whole slide 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
- Quantitation of immunohistochemical-stained histopathology slides
- Correlation of histopathology slides with other markers of disease.
- 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
All procedures are well-documented and adhere to standard operating procedures (SOPs), methods, or study-approved plans and agreements.
- 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.