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Cited by in F6Publishing
For: Zhang Z, Coyle JL, Sejdić E. Automatic hyoid bone detection in fluoroscopic images using deep learning. Sci Rep 2018;8:12310. [PMID: 30120314 DOI: 10.1038/s41598-018-30182-6] [Cited by in Crossref: 18] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Lee KS, Lee E, Choi B, Pyun SB. Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks. Diagnostics (Basel) 2021;11:300. [PMID: 33668528 DOI: 10.3390/diagnostics11020300] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Mao S, Sabry A, Khalifa Y, Coyle JL, Sejdic E. Estimation of laryngeal closure duration during swallowing without invasive X-rays. Future Gener Comput Syst 2021;115:610-8. [PMID: 33100445 DOI: 10.1016/j.future.2020.09.040] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
3 Shu K, Perera S, Mahoney AS, Mao S, Coyle JL, Sejdić E. Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection. Laryngoscope 2022. [PMID: 35657100 DOI: 10.1002/lary.30222] [Reference Citation Analysis]
4 Feng S, Shea QT, Ng KY, Tang CN, Kwong E, Zheng Y. Automatic Hyoid Bone Tracking in Real-Time Ultrasound Swallowing Videos Using Deep Learning Based and Correlation Filter Based Trackers. Sensors (Basel) 2021;21:3712. [PMID: 34073586 DOI: 10.3390/s21113712] [Reference Citation Analysis]
5 Donohue C, Khalifa Y, Perera S, Sejdić E, Coyle JL. A Preliminary Investigation of Whether HRCA Signals Can Differentiate Between Swallows from Healthy People and Swallows from People with Neurodegenerative Diseases. Dysphagia 2021;36:635-43. [PMID: 32889627 DOI: 10.1007/s00455-020-10177-0] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
6 Kim HI, Kim Y, Kim B, Shin DY, Lee SJ, Choi SI. Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network. Diagnostics (Basel) 2021;11:1147. [PMID: 34201839 DOI: 10.3390/diagnostics11071147] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Hsiao MY, Weng CH, Wang YC, Cheng SH, Wei KC, Tung PY, Chen JY, Yeh CY, Wang TG. Deep Learning for Automatic Hyoid Tracking in Videofluoroscopic Swallow Studies. Dysphagia 2022. [PMID: 35482213 DOI: 10.1007/s00455-022-10438-0] [Reference Citation Analysis]
8 Coyle JL, Sejdić E. High-Resolution Cervical Auscultation and Data Science: New Tools to Address an Old Problem. Am J Speech Lang Pathol 2020;29:992-1000. [PMID: 32650655 DOI: 10.1044/2020_AJSLP-19-00155] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Lee JT, Park E, Hwang JM, Jung TD, Park D. Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study. Sci Rep 2020;10:14735. [PMID: 32895465 DOI: 10.1038/s41598-020-71713-4] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
10 Fujinaka A, Mekata K, Takizawa H, Kudo H. Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection. Int J Comput Assist Radiol Surg 2020;15:901-8. [PMID: 32306186 DOI: 10.1007/s11548-020-02145-8] [Reference Citation Analysis]
11 Zhang Z, Mao S, Coyle J, Sejdić E. Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning. Med Image Anal 2021;74:102218. [PMID: 34487983 DOI: 10.1016/j.media.2021.102218] [Reference Citation Analysis]
12 Lee JT, Park E, Jung TD. Automatic Detection of the Pharyngeal Phase in Raw Videos for the Videofluoroscopic Swallowing Study Using Efficient Data Collection and 3D Convolutional Networks . Sensors (Basel) 2019;19:E3873. [PMID: 31500332 DOI: 10.3390/s19183873] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 2.3] [Reference Citation Analysis]
13 Guo J, Chen J, Wang J, Ren G, Tian Q, Guo C. EMG-assisted forward dynamics simulation of subject-specific mandible musculoskeletal system. Journal of Biomechanics 2022. [DOI: 10.1016/j.jbiomech.2022.111143] [Reference Citation Analysis]
14 Donohue C, Khalifa Y, Perera S, Sejdić E, Coyle JL. How Closely do Machine Ratings of Duration of UES Opening During Videofluoroscopy Approximate Clinician Ratings Using Temporal Kinematic Analyses and the MBSImP? Dysphagia 2021;36:707-18. [PMID: 32955619 DOI: 10.1007/s00455-020-10191-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
15 Ito E, Matsuda Y, Kuroda M, Araki K. A novel dysphagia screening method using panoramic radiography. Showa Univ J Med Sci 2021;33:74-81. [DOI: 10.15369/sujms.33.74] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Donohue C, Mao S, Sejdić E, Coyle JL. Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals. Dysphagia 2021;36:259-69. [PMID: 32419103 DOI: 10.1007/s00455-020-10124-z] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
17 Lee WH, Lim MH, Seo HG, Oh BM, Kim S. Hyoid kinematic features for poor swallowing prognosis in patients with post-stroke dysphagia. Sci Rep 2021;11:1471. [PMID: 33446787 DOI: 10.1038/s41598-020-80871-4] [Reference Citation Analysis]