[This retracts the article DOI: 10.1155/2022/3176134.].
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http://dx.doi.org/10.1155/2023/9847843 | DOI Listing |
J Shoulder Elbow Surg
December 2024
School for Biomedical and Precision Engineering, Personalised Medicine Research, University of Bern, Bern, Switzerland.
Background: Tear size and shape are known to prognosticate the efficacy of surgical rotator cuff (RC) repair however, current manual measurements on magnetic resonance images (MRI), exhibit high interobserver variabilities and exclude three-dimensional (3D) morphological information. This study aimed to develop algorithms for automatic 3D analyses of posterosuperior full-thickness RC tear to enable efficient and precise tear evaluation and 3D tear visualization.
Methods: - A deep-learning network for automatic segmentation of the tear region in coronal and sagittal multicenter MRI was trained with manually segmented (consensus of 3 experts) pd- and T2 weighted MRI of shoulders with full-thickness posterosuperior tears (n=200).
BMC Cardiovasc Disord
November 2024
Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Sensors (Basel)
November 2024
Yangxian Guangda New Energy Machinery Co., Ltd., Hanzhong 723300, China.
Sci Rep
November 2024
Department of Financial Technologies, Financial University Under the Government of the Russian Federation, Moscow, 125993, Russia.
Cureus
October 2024
Department of Anesthesiology, Uniformed Services University of the Health Sciences, Bethesda, USA.
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