Detail

Keywords

Amyloidosis; Artificial Intelligence; Automatic Data Processing; Cardiac Imaging Techniques; Computational Biology; Data Mining; Decision Making, Computer-Assisted; Electronic Health Records; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Medical Informatics; Nuclear Medicine; Pattern Recognition, Automated

Research group(s)

• Christian Doppler Laboratory for Applied Metabolomics
  Head: Lukas Kenner
  Research Area: We investigate ways to better characterize tumors using non-invasive imaging techniques. This is important because tumours are constantly changing through mutations. In this way, an individual therapy should be possible and its success should be continuously monitored.
  Members:
  Marko Grahovac
  Kilian Kluge
  Lukas Kenner
  Clemens P. Spielvogel
  David Haberl
  Jing Ning
• Data Science and Analysis in Nuclear Medicine and Molecular Imaging
  Head:
  Members:
  Clemens P. Spielvogel
  David Haberl

Research interests

Discovery, development, and clinical assessment of novel biomarkers and clinical decision support systems.

Focus areas include nuclear medicine, oncological, and cardiovascular diseases.

Techniques, methods & infrastructure

• Imaging marker quantification
• Machine learning
• Vision-based deep learning
• Explainable artificial intelligence
• Automated image segmentation
• Imaging / non-imaging data integration

Selected publications

1. 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.
2. Haberl, D. et al. (2024) ‘Multicenter PET image harmonization using generative adversarial networks’, European Journal of Nuclear Medicine and Molecular Imaging. Available at: https://doi.org/10.1007/s00259-024-06708-8.
3. Spielvogel, C.P. et al. (2022) ‘Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer’, European Journal of Nuclear Medicine and Molecular Imaging, 50(2), pp. 546–558. Available at: https://doi.org/10.1007/s00259-022-05973-9.
4. Papp, L. et al. (2023) ‘DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters’, Neural Networks, 167, pp. 517–532. Available at: https://doi.org/10.1016/j.neunet.2023.08.026.
5. Papp, L. et al. (2020) ‘Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI’, European Journal of Nuclear Medicine and Molecular Imaging, 48(6), pp. 1795–1805. Available at: https://doi.org/10.1007/s00259-020-05140-y.