Nathan Armani, M.Sc., Ph.D. - Directory@UAlberta.ca

Contact

Faculty of Medicine & Dentistry - Medicine Dept

Email: narmani@ualberta.ca

Overview

Area of Study / Keywords

Machine Learning; Quantitative Image Processing; Transitional Medicine

About

Dr. Armani’s research is situated at the nexus of translational medicine, radiomics, and health data science, with a primary focus on advancing precision diagnostics. His research program applies quantitative image processing, machine learning, and statistical modeling to enhance the detection, characterization, and monitoring of complex medical conditions. He has led and contributed to studies integrating quantitative imaging with administrative health data and clinical trial datasets, enabling robust population-level modeling and predictive analytics. Leveraging principles of implementation science, his work investigates strategies to embed data-driven diagnostic tools into clinical and health system workflows. By integrating biomedical engineering, medical imaging, and large-scale health data, Dr. Armani’s research seeks to accelerate the translation of precision diagnostics into evidence-based healthcare practice.

Research

Dr. Armani’s research program integrates machine learning, quantitative image processing, and health data science to advance precision diagnostics and improve the characterization and monitoring of complex medical conditions. It applies machine learning, quantitative MRI and CT image analysis, and radiomic feature extraction to identify novel biomarkers and physiological patterns associated with clinical outcomes. The work spans large-scale administrative health data, clinical trials, and predictive modeling, enabling population-level analyses and the development of data-driven tools. Grounded in implementation science, the program also investigates strategies for integrating these diagnostic tools into clinical workflows, supporting the translation of research discoveries into evidence-based healthcare practice.

Featured Publications

ISSLS Prize in Bioengineering Science 2026: Hidden in Plain Sight: Machine Learning–Assisted MRI Reveals Novel Vertebral Body Biomarkers of Chronic Low Back Pain in Humans

European Spine Journal. 2026 March; 35

Assessing the Effects of Immune Checkpoint Inhibitors on Bone Utilizing Machine Learning-assisted Opportunistic Quantitative Computed Tomography

Journal of Bone and Mineral Research. 2025 January;

Use of machine learning to select texture features in investigating the effects of axial loading on T2-maps from magnetic resonance imaging of the lumbar discs

European Spine Journal. 2021 October; 10.1007/s00586-021-07036-3

Texture analysis in the classification of T2‐weighted magnetic resonance images in persons with and without low back pain

Journal of Orthopaedic Research. 2021 October; 10.1002/jor.24930

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