Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence

Croci, Eleonora; Hess, Hanspeter; Warmuth, Fabian; Künzler, Marina; Börlin, Sean; Baumgartner, Daniel; Müller, Andreas Marc; Gerber, Kate; Mündermann, Annegret

doi:10.1007/s00330-023-10082-8

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30245

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence
Authors:	Croci, Eleonora Hess, Hanspeter Warmuth, Fabian Künzler, Marina Börlin, Sean Baumgartner, Daniel Müller, Andreas Marc Gerber, Kate Mündermann, Annegret
et. al:	No
DOI:	10.1007/s00330-023-10082-8 10.21256/zhaw-30245
Published in:	European Radiology
Volume(Issue):	34
Page(s):	270
Pages to:	278
Issue Date:	11-Aug-2023
Publisher / Ed. Institution:	Springer
ISSN:	0938-7994 1432-1084
Language:	English
Subjects:	Shoulder; Rotator cuff injury; Fluoroscopy; Artificial intelligence; Motion
Subject (DDC):	006: Special computer methods 616.7: Diseases of musculoskeletal system and orthopaedics
Abstract:	Objective: Patients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head. Materials and methods: Fluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere. Results: The nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°. Conclusion: The proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane. Clinical relevance statement: This nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders. Key Points: • Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application.
Further description:	Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)
URI:	https://digitalcollection.zhaw.ch/handle/11475/30245
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Mechanical Systems (IMES)
Appears in collections:	Publikationen School of Engineering

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Croci, E., Hess, H., Warmuth, F., Künzler, M., Börlin, S., Baumgartner, D., Müller, A. M., Gerber, K., & Mündermann, A. (2023). Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence. European Radiology, 34, 270–278. https://doi.org/10.1007/s00330-023-10082-8

Croci, E. et al. (2023) ‘Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence’, European Radiology, 34, pp. 270–278. Available at: https://doi.org/10.1007/s00330-023-10082-8.

E. Croci et al., “Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence,” European Radiology, vol. 34, pp. 270–278, Aug. 2023, doi: 10.1007/s00330-023-10082-8.

CROCI, Eleonora, Hanspeter HESS, Fabian WARMUTH, Marina KÜNZLER, Sean BÖRLIN, Daniel BAUMGARTNER, Andreas Marc MÜLLER, Kate GERBER und Annegret MÜNDERMANN, 2023. Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence. European Radiology. 11 August 2023. Bd. 34, S. 270–278. DOI 10.1007/s00330-023-10082-8

Croci, Eleonora, Hanspeter Hess, Fabian Warmuth, Marina Künzler, Sean Börlin, Daniel Baumgartner, Andreas Marc Müller, Kate Gerber, and Annegret Mündermann. 2023. “Fully Automatic Algorithm for Detecting and Tracking Anatomical Shoulder Landmarks on Fluoroscopy Images with Artificial Intelligence.” European Radiology 34 (August): 270–78. https://doi.org/10.1007/s00330-023-10082-8.

Croci, Eleonora, et al. “Fully Automatic Algorithm for Detecting and Tracking Anatomical Shoulder Landmarks on Fluoroscopy Images with Artificial Intelligence.” European Radiology, vol. 34, Aug. 2023, pp. 270–78, https://doi.org/10.1007/s00330-023-10082-8.