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For: Weber KA 2nd, Abbott R, Bojilov V, Smith AC, Wasielewski M, Hastie TJ, Parrish TB, Mackey S, Elliott JM. Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions. Sci Rep 2021;11:16567. [PMID: 34400672 DOI: 10.1038/s41598-021-95972-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Elliott JM, Walton DM, Albin SR, Courtney DM, Siegmund GP, Carroll LJ, Weber KA 2nd, Smith AC. Biopsychosocial Sequelae and Recovery Trajectories From Whiplash Injury Following a Motor Vehicle Collision. Spine J 2023:S1529-9430(23)00108-0. [PMID: 36958668 DOI: 10.1016/j.spinee.2023.03.005] [Reference Citation Analysis]
2 Bonaldi L, Pretto A, Pirri C, Uccheddu F, Fontanella CG, Stecco C. Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies. Bioengineering (Basel) 2023;10. [PMID: 36829631 DOI: 10.3390/bioengineering10020137] [Reference Citation Analysis]
3 Paskali F, Simantzik J, Dieterich A, Kohl M. Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning. Diagnostics (Basel) 2022;13. [PMID: 36611299 DOI: 10.3390/diagnostics13010007] [Reference Citation Analysis]
4 Snodgrass SJ, Stanwell P, Weber KA, Shepherd S, Kennedy O, Thompson HJ, Elliott JM. Greater muscle volume and muscle fat infiltrate in the deep cervical spine extensor muscles (multifidus with semispinalis cervicis) in individuals with chronic idiopathic neck pain compared to age and sex-matched asymptomatic controls: a cross-sectional study. BMC Musculoskelet Disord 2022;23:973. [PMID: 36357864 DOI: 10.1186/s12891-022-05924-3] [Reference Citation Analysis]
5 Naghdi N, Elliott JM, Weber MH, Fehlings MG, Fortin M. Morphological Changes of Deep Extensor Neck Muscles in Relation to the Maximum Level of Cord Compression and Canal Compromise in Patients With Degenerative Cervical Myelopathy. Global Spine J 2022;:21925682221136492. [PMID: 36289049 DOI: 10.1177/21925682221136492] [Reference Citation Analysis]
6 Gao KT, Tibrewala R, Hess M, Bharadwaj UU, Inamdar G, Link TM, Chin CT, Pedoia V, Majumdar S. Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning. JOR Spine 2022;5. [DOI: 10.1002/jsp2.1204] [Reference Citation Analysis]
7 Rummens S, Bosch S, Dierckx S, Vanmechelen A, Peeters R, Brumagne S, Desloovere K, Peers K. Reliability and agreement of lumbar multifidus volume and fat fraction quantification using magnetic resonance imaging. Musculoskelet Sci Pract 2022;59:102532. [PMID: 35245881 DOI: 10.1016/j.msksp.2022.102532] [Reference Citation Analysis]
8 Franettovich Smith MM, Mendis MD, Weber KA 2nd, Elliott JM, Ho R, Wilkes MJ, Collins NJ. Improving the measurement of intrinsic foot muscle morphology and composition from high-field (7T) magnetic resonance imaging. J Biomech 2022;140:111164. [PMID: 35661535 DOI: 10.1016/j.jbiomech.2022.111164] [Reference Citation Analysis]
9 Bodkin SG, Smith AC, Bergman BC, Huo D, Weber KA, Zarini S, Kahn D, Garfield A, Macias E, Harris-love MO. Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults. Front Rehabilit Sci 2022;3. [DOI: 10.3389/fresc.2022.808538] [Reference Citation Analysis]