Department of Biomedical Imaging and Image-guided Therapy (Clinical Division of Nuclear Medicine)
Position: Research Associate (Postdoc)
ORCID: 0000-0002-4409-8324
T +43 1 40400 72350
clemens.spielvogel@muv.ac.at
Keywords
Amyloidosis; Artificial Intelligence; Atherosclerosis; Automatic Data Processing; Cardiac Imaging Techniques; Computational Biology; Data Analysis; Data Interpretation, Statistical; Data Mining; Data Science; Decision Making, Computer-Assisted; Electronic Health Records; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Medical Informatics; Molecular Imaging; Multimodal Imaging; Nuclear Medicine; Pattern Recognition, Automated; Positron-Emission Tomography
Research group(s)
- Computational Nuclear Medicine
Head:
Members:
Research interests
Discovery, development, and clinical assessment of novel biomarkers and clinical decision support systems.
Primary focus areas include nuclear imaging and cardiovascular disease.
Techniques, methods & infrastructure
- Machine learning
- Vision-based deep learning
- Explainable artificial intelligence
- Cardiovascular imaging
- Quantitative imaging markers
- Imaging / non-imaging data integration
- Opportunistic risk markers
Grants
- AI-enabled SPECT/CT imaging for cardiac amyloidosis (as sole Principal Investigator) (2025)
Source of Funding: FWF (Austrian Science Fund), FWF Erwin Schroedinger Fellowship Grant
Principal Investigator - Markers for the detection of cardiac amyloidosis (as sole Principal Investigator) (2025)
Source of Funding: Pfizer, Contract Agreement
Principal Investigator - Christian Doppler Laboratory (CDL) for Applied Metabolomics (as Sub-Investigator) (2019)
Source of Funding: CDG (Christian Doppler Research Association), Christian Doppler Laboratory
Principal Investigator
Selected publications
- Spielvogel, C.P. et al. (2024) ‘Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study’, The Lancet Digital Health, 6(4), pp. e251–e260. Available at: https://doi.org/10.1016/s2589-7500(23)00265-0.
- Spielvogel, C.P. et al. (2024) ‘Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [68Ga]Ga-PSMA-11 PET/MRI’, Insights Into Imaging. Available at: https://doi.org/10.1186/s13244-024-01876-5.
- Haberl, D. et al. (2025) ‘Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments’, European Journal of Nuclear Medicine and Molecular Imaging, 52(7), pp. 2355–2368. Available at: https://doi.org/10.1007/s00259-025-07091-8.
- Spielvogel, C.P. et al. (2025) ‘Impact of disease-modifying therapy on [99mTc]Tc-DPD SPECT/CT markers in transthyretin cardiac amyloidosis enabled by artificial intelligence’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: https://doi.org/10.1007/s00259-025-07673-6.
- Spielvogel, C.P. et al. (2025) ‘Artificial intelligence-enabled opportunistic identification of immune checkpoint inhibitor-related adverse events using [18F]FDG PET/CT’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: https://doi.org/10.1007/s00259-025-07364-2.