Ruckli, Adrian Cyrill

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2023

Meier, Malin Kristin; Scheuber, Samira; Hanke, Markus Simon; Haefeli, Pascal Cyrill; Ruckli, Adrian Cyrill; Liechti, Emanuel Francis; Gerber, Nicolas; Lerch, Till Dominic; Tannast, Moritz; Siebenrock, Klaus Arno; Steppacher, Simon Damian; Schmaranzer, Florian (2023). Does the dGEMRIC Index Recover 3 Years After Surgical FAI Correction and an Initial dGEMRIC Decrease at 1-Year Follow-up? A Controlled Prospective Study. The American journal of sports medicine, 51(7), pp. 1808-1817. Sage 10.1177/03635465231167854

Ruckli, Adrian C.; Nanavati, Andreas K.; Meier, Malin K.; Lerch, Till D.; Steppacher, Simon D.; Vuilleumier, Sébastian; Boschung, Adam; Vuillemin, Nicolas; Tannast, Moritz; Siebenrock, Klaus A.; Gerber, Nicolas; Schmaranzer, Florian (2023). A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making. Journal of personalized medicine, 13(1), p. 153. MDPI 10.3390/jpm13010153

Hess, Hanspeter; Ruckli, Adrian C.; Bürki, Finn; Gerber, Nicolas; Menzemer, Jennifer; Burger, Jürgen; Schär, Michael; Zumstein, Matthias A.; Gerber, Kate (2023). Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction. Diagnostics, 13(10), p. 1668. MDPI 10.3390/diagnostics13101668

2022

Ruckli, Adrian Cyrill; Schmaranzer, Florian; Meier, Malin K; Lerch, Till D; Steppacher, Simon D; Tannast, Moritz; Zeng, Guodong; Burger, Jürgen; Siebenrock, Klaus A; Gerber, Nicolas; Gerber, Kate (2022). Automated quantification of cartilage quality for hip treatment decision support. International journal of computer assisted radiology and surgery, 17(11), pp. 2011-2021. Springer 10.1007/s11548-022-02714-z

2021

Hess, Hanspeter; Zumstein, M.; Dommer, L.; Schär, M.; Hayoz, A.; Zeng, Guodong; Ruckli, Adrian Cyrill; Burger, Jürgen; Gerber, Nicolas; Gerber, Kate (4 June 2021). Automatic shoulder bone segmentation from CT arthrograms based on deep learning. International Journal of Computer Assisted Radiology and Surgery, 16(S1), pp. 90-91. Springer

Ruckli, Adrian Cyrill; Schmaranzer, F.; Lerch, T.; Boschung, A.; Steppacher, S.; Burger, Jürgen; Tannast, M.; Siebenrock, K.; Gerber, Nicolas; Gerber, Kate (4 June 2021). Deep learning for automatic quantification of AVN of the femoral head on 3D MRI in patients eligible for joint preserving surgery: A pilot study. International Journal of Computer Assisted Radiology and Surgery, 16(S1), S85-S86. Springer-Verlag

Boschung, Adam; Ruckli, Adrian; Lerch, Till; Steppacher, Simon; Gerber, Nicolas; Gerber, Kate; Burger, Jürgen; Tannast, Moritz; Siebenrock, Klaus-Arno; Schmaranzer, Florian (June 2021). Deep Learning For Fully-Automatic Quantification Of Avascular Necrosis Of The Femoral Head On 3D Hip MRI In Young Patients Eligible For Joint Preserving Hip Surgery: A Pilot Study. In: EFORT Virtual Congress (European Federation of Orthopaedics and Traumatology).

Ruckli, Adrian Cyrill; Schmaranzer, Florian; Lerch, Till; Boschung, Adam; Steppacher, Simon; Burger, Jürgen; Tannast, Moritz; Siebenrock, Klaus; Gerber, Kate; Gerber, Nicolas (17 May 2021). Deep learning for fully-automatic quantification of avascular necrosis of the femoral head on 3D hip MRI in young patients eligible for joint preserving hip surgery: A pilot study (Unpublished). In: Bern Data Science Day 2021. Bern. 23.04.2021. 10.5281/zenodo.4767398

Ruckli, Adrian Cyrill; Lerch, Till; Boschung, Adam; Steppacher, Simon; Gerber, Nicolas; Gerber, Kate; Burger, Jürgen; Tannast, Moritz; Siebenrock, Klaus; Schmaranzer, Florian (April 2021). Deep learning for fully-automatic quantification of avascular necrosis of the femoral head on 3D hip MRI in young patients eligible for joint preserving hip surgery: A pilot study. Skeletal radiology, 50, p. 1060. Springer

Ruckli, Adrian Cyrill; Vuilleumier, S; Boschung, A; Lerch, Till; Steppacher, Simon Damian; Tannast, Moritz; Siebenrock, Klaus-Arno; Burger, Jürgen; Gerber, Nicolas; Schmaranzer, Florian (2021). Deep learning for fully-automatic quantification of avascular necrosis of the femoral head on 3D hip MRI in young patients eligible for joint preserving hip surgery: A pilot study (Unpublished). In: SCR (Swiss Congress of Radiology).

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