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Cited by in F6Publishing
For: Eastwood P, Gilani SZ, McArdle N, Hillman D, Walsh J, Maddison K, Goonewardene M, Mian A. Predicting sleep apnea from three-dimensional face photography. J Clin Sleep Med 2020;16:493-502. [PMID: 32003736 DOI: 10.5664/jcsm.8246] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
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1 Tsuiki S, Nagaoka T, Fukuda T, Sakamoto Y, Almeida FR, Nakayama H, Inoue Y, Enno H. Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study. Sleep Breath 2021. [PMID: 33559004 DOI: 10.1007/s11325-021-02301-7] [Reference Citation Analysis]
2 Lee JJ, Sundar KM. Evaluation and Management of Adults with Obstructive Sleep Apnea Syndrome. Lung 2021;199:87-101. [PMID: 33713177 DOI: 10.1007/s00408-021-00426-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
3 Ohmura K, Suzuki M, Soma M, Yamazaki S, Uchida Y, Komiyama K, Shirahata T, Miyashita T, Nagata M, Nakamura H. Predicting the presence and severity of obstructive sleep apnea based on mandibular measurements using quantitative analysis of facial profiles via three-dimensional photogrammetry. Respir Investig 2021:S2212-5345(21)00182-9. [PMID: 34810147 DOI: 10.1016/j.resinv.2021.10.002] [Reference Citation Analysis]
4 Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021;59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Reference Citation Analysis]