Dr. Norman Juchler

Network

ORCID digital identifier

ORCID ID: 0000-0001-8518-7211

Projects

• GEMINI: A Generation of Multi-scale Digital Twins of Ischaemic and Haemorrhagic Stroke Patients / Deputy project leader / ongoing
• Stroke DynamiX – Graphical and Causal Networks for Personalized Stroke Management / Team member / completed
• OSR4H – Open Set Recognition for Hematology / Team member / completed
• Deep Brain Vessel Profiler / Team member / completed
• AneuX Modeling – Shape as a Biomarker for Instability of Intracranial Aneurysms / Team member / completed

Publications

Articles in scientific journal, peer-reviewed

• Musio, F. et al. (2026) 'Circle of Willis centerline graphs : a dataset and baseline algorithm', Neuroscience Informatics, 6(1), p. 100265. doi: 10.1016/j.neuri.2026.100265.
• Reissenberger, P. et al. (2023) 'Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial : detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor', European Heart Journal - Digital Health, 4(5), pp. 402–410. doi: 10.1093/ehjdh/ztad039.
• Bächinger, D. et al. (2023) 'Radiological feature heterogeneity supports etiological diversity among patient groups in Meniere's disease', Scientific Reports, 13(1), p. 10303. doi: 10.1038/s41598-023-36479-5.
• Mack, I. et al. (2022) 'Wearable technologies for pediatric patients with surgical infections : more than counting steps?', Biosensors, 12(8), p. 634. doi: 10.3390/bios12080634.
• Juchler, N. et al. (2022) 'Shape trumps size : image-based morphological analysis reveals that the 3D shape discriminates intracranial aneurysm disease status better than aneurysm size', Frontiers in Neurology, 13(809391). doi: 10.3389/fneur.2022.809391.
• Juchler, N. et al. (2020) 'Shape irregularity of the intracranial aneurysm lumen exhibits diagnostic value', Acta Neurochirurgica, 162(9), pp. 2261–2270. doi: 10.1007/s00701-020-04428-0.
• Juchler, N. et al. (2020) 'Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms', Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization, 8(5), pp. 538–546. doi: 10.1080/21681163.2020.1728579.
• Detmer, F. J. et al. (2019) 'Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics', Neurosurgical Focus, 47(1). doi: 10.3171/2019.4.FOCUS19145.
• Detmer, F. J. et al. (2018) 'External validation of cerebral aneurysm rupture probability model with data from two patient cohorts', Acta Neurochirurgica. doi: 10.1007/s00701-018-3712-8.

Books, peer-reviewed

Juchler, N. (2020) Shape-based analysis of intracranial aneurysms. Doctoral dissertation. University of Zurich. doi: 10.5167/uzh-200995.

Written conference contributions, peer-reviewed

• Dupuy, N. et al. (2022) 'Exploring intracranial aneurysm instability markers to improve disease modeling', in Nithiarasu, P. and Vergara, C. (eds) CMBE 2022 : 7th International Conference on Computational & Mathematical Biomedical Engineering. Computational & Mathematical Biomedical Engineering, pp. 14–17. doi: 10.21256/zhaw-25387.
• Juchler, N., Bijlenga, P. and Hirsch, S. (2022) 'Modeling the location-dependency of aneurysm shape : a morphometric comparative study', in Nithiarasu, P. and Vergara, C. (eds) CMBE 2022 : 7th International Conference on Computational & Mathematical Biomedical Engineering. Computational & Mathematical Biomedical Engineering, pp. 703–705. doi: 10.21256/zhaw-25388.
• Juchler, N., Bijlenga, P. and Hirsch, S. (2022) 'The role of shape for aneurysm risk assessment', in Nithiarasu, P. and Vergara, C. (eds) CMBE 2022 : 7th International Conference on Computational & Mathematical Biomedical Engineering. Computational & Mathematical Biomedical Engineering, pp. 84–86. doi: 10.21256/zhaw-25386.
• Watanabe, K. et al. (2020) 'Influence of input image configurations on output of a convolutional neural network to detect cerebral aneurysms', in International Mechanical Engineering Congress and Exposition : Volume 3 - biomedical and biotechnology engineering. The American Society of Mechanical Engineers. doi: 10.1115/IMECE2019-11125.
• Juchler, N. et al. (2019) 'Understanding morphological irregularity : a rater-based study', in CMBE19 Proceedings. Cardiff: Zeta Computational Resources, p. 583.
• Juchler, N. et al. (2019) 'Identification of clinically relevant characteristics of intracranial aneurysm morphology', in 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
• Juchler, N. et al. (2017) 'Measuring the perceived morphological complexity of intracranial aneurysms', in Abstract Book, 3rd International SystemsX.ch Conference on Systems Biology, p. 160. doi: 10.3929/ethz-b-000176201.
• Juchler, N. et al. (2016) 'Shape-based assessment of intracranial aneurysm disease status – a machine learning approach', in All SystemsX.ch Day, Bern, 1 September 2016.
• Juchler, N. et al. (2016) 'What's ugly? : shape-based analysis of intracranial aneurysms', in Latsis Symposium 2016 : Personalized Medicine, Zurich, 27-29 June 2016.
• Juchler, N. et al. (2015) 'On the utility of 3D Zernike Moment Invariants to assess aneurysm disease status', in All SystemsX.ch Day, Bern, 15 September 2015.

Other publications

Juchler, N. et al. (2016) 'Shape-based assessment of intracranial aneurysm disease status', in ZNZ Symposium 2016, Zurich, 15. September 2016.

Oral conference contributions and abstracts

• Musio, F. et al. (2023) 'Circle of Willis configurations in stroke patients', in 19th Interdisciplinary Cerebrovascular Symposium, Geneva, Switzerland, 17-19 August 2023.
• Hirsch, S. and Juchler, N. (2019) 'Real and assumed insights : statistical models and imaging biomarkers for disease characterization of intracranial aneurysms', in 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019.
• Watanabe, K. et al. (2018) 'Effect of input image representation for results of neural network to detect cerebral aneurysms', in 31st Bioengineering Conference / 2018 Annual Meeting of BED/JSME, Koriyama, Japan, 14-15 December 2018.
• Juchler, N. et al. (2018) 'Clinical data sharing : a data scientist's perspective', in Life in Numbers 4, Wädenswil, 4 October 2018.
• Juchler, N. et al. (2018) 'Reproducing qualitative irregularity ratings by means of quantitative shape descriptors in intracranial aneurysms', in 8th World Congress of Biomechanics (WCB2018), Dublin, Ireland, 8-12 July 2018.
• Juchler, N. et al. (2018) 'Aneurysm shape as a diagnostic tool', in Kick-Off Event: PhD Network in Data Science, Winterthur, 13. April 2018.
• Anzai, H. et al. (2018) 'Correlation of CFD with wall enhancement', in iNEW, Zürich, 8. Februar 2018.
• Juchler, N. et al. (2018) 'Aneurysm shape as a diagnostic tool : a machine learning approach', in International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018.
• Hirsch, S. et al. (2018) 'Using machine learning to identify shape biomarkers in intracranial aneurysm', in Life Science Zurich Impact, Zürich, 22 January 2018.
• Hirsch, S. et al. (2017) 'Big Data : machine learning to identify shape biomarkers in intracranial aneurysm', in Annual Meeting of the Swiss Society for Biomedical Engineering (SSBE), Winterthur, 30 August 2017.
• Anzai, H. et al. (2017) 'Where does CFD identify lesion instability in small aneurysms?', in CMBE17 : 5th International Conference on Computational and Mathematical Biomedical Engineering.
• Juchler, N. et al. (2017) 'Using shape descriptors to categorize intracranial aneurysms', in CMBE17 : 5th International Conference on Computational and Mathematical Biomedical Engineering.
• Juchler, N. et al. (2016) 'Shape-based modeling of aneurysmal disease status', in ECCOMAS Congress 2016, Heraklion, Greece, 5-10 June 2016.

Research data

Juchler, Norman; Bijlenga, Philippe; Hirsch, Sven, 2022. AneuX morphology database. Zenodo. Available from: https://doi.org/10.5281/zenodo.6678442

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