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Cited by in F6Publishing
For: 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: 10] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Chang MC, Choi HY, Park D. Usefulness of the Modified Videofluoroscopic Dysphagia Scale in Determining the Allowance of Oral Feeding in Patients with Dysphagia Due to Deconditioning or Frailty. Healthcare 2022;10:668. [DOI: 10.3390/healthcare10040668] [Reference Citation Analysis]
3 Kim JK, Lv Z, Park D, Chang MC. Practical Machine Learning Model to Predict the Recovery of Motor Function in Patients with Stroke. Eur Neurol 2022;:1-7. [PMID: 35350014 DOI: 10.1159/000522254] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Kim JK, Chang MC, Park D. Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct. Front Neurosci 2021;15:795553. [PMID: 35046770 DOI: 10.3389/fnins.2021.795553] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Lee BJ, Eo H, Park D. Usefulness of the Modified Videofluoroscopic Dysphagia Scale in Evaluating Swallowing Function among Patients with Amyotrophic Lateral Sclerosis and Dysphagia. J Clin Med 2021;10:4300. [PMID: 34640316 DOI: 10.3390/jcm10194300] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [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: 2] [Reference Citation Analysis]
7 Lee BJ, Eo H, Lee C, Park D. Usefulness of the Modified Videofluoroscopic Dysphagia Scale in Choosing the Feeding Method for Stroke Patients with Dysphagia. Healthcare (Basel) 2021;9:632. [PMID: 34071752 DOI: 10.3390/healthcare9060632] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
8 Nakamura A, Saito T, Ikeda D, Ohta K, Mineno H, Nishimura M. Automatic Detection of Chewing and Swallowing. Sensors (Basel) 2021;21:3378. [PMID: 34066269 DOI: 10.3390/s21103378] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
9 Chang MC, Park S, Cho JY, Lee BJ, Hwang JM, Kim K, Park D. Comparison of three different types of exercises for selective contractions of supra- and infrahyoid muscles. Sci Rep 2021;11:7131. [PMID: 33785793 DOI: 10.1038/s41598-021-86502-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
10 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]