Zacytuj

Mayil M, Keser G, Demir A, Pekiner FN: Assessment of masseter muscle appearance and thickness in edentulous and dentate patients by ultrasonography. Open Dent J 2018; 12: 723–734. Mayil M, Keser G, Demir A, Pekiner FN: Assessment of masseter muscle appearance and thickness in edentulous and dentate patients by ultrasonography. Open Dent J 2018; 12: 723734.10.2174/1745017901814010723618288430369982Search in Google Scholar

Schellhas KP: MR imaging of muscles of mastication. AJR Am J Roentgenol 1989; 153: 847–855. Schellhas KP: MR imaging of muscles of mastication. AJR Am J Roentgenol 1989; 153: 847855.10.2214/ajr.153.4.8472773742Search in Google Scholar

Norton NS, Netter FH: Netter’s head and neck anatomy for dentistry. 2nd ed. WB Saunders, Philadelphia 2012: 223–225. Norton NS, Netter FH: Netter’s head and neck anatomy for dentistry. 2nd ed. WB Saunders, Philadelphia 2012: 223225.Search in Google Scholar

Weijs WA, Hillen B: Correlations between the cross-sectional area of the jaw muscles and craniofacial size and shape. Am J Phys Anthropol 1986; 70: 423–431. Weijs WA, Hillen B: Correlations between the cross-sectional area of the jaw muscles and craniofacial size and shape. Am J Phys Anthropol 1986; 70: 423431.10.1002/ajpa.13307004033766712Search in Google Scholar

Reis Durão AP, Morosolli A, Brown J, Jacobs R: Masseter muscle measurement performed by ultrasound: a systematic review. Dentomaxillofac Radiol 2017; 46: 20170052. Reis Durão AP, Morosolli A, Brown J, Jacobs R: Masseter muscle measurement performed by ultrasound: a systematic review. Dentomaxillofac Radiol 2017; 46: 20170052.10.1259/dmfr.20170052560628528467130Search in Google Scholar

Weijs WA, Hillen B: Correlations between the cross-sectional area of the jaw muscles and craniofacial size and shape. Am J Phys Anthropol 1986; 70: 423–431. Weijs WA, Hillen B: Correlations between the cross-sectional area of the jaw muscles and craniofacial size and shape. Am J Phys Anthropol 1986; 70: 423431.10.1002/ajpa.1330700403Search in Google Scholar

Uchida Y, Motoyoshi M, Shigeeda T, Shinohara A, Igarashi Y, Sakaguchi M et al.: Relationship between masseter muscle size and maxillary morphology. Eur J Orthod 2011; 33: 654–659. Uchida Y, Motoyoshi M, Shigeeda T, Shinohara A, Igarashi Y, Sakaguchi M et al.: Relationship between masseter muscle size and maxillary morphology. Eur J Orthod 2011; 33: 654659.10.1093/ejo/cjq15221262936Search in Google Scholar

Kiliaridis S, Kalebo P: Masseter muscle thickness measured by ultrasonography and its relation to facial morphology. J Dent Res 1991; 70: 1262–1265. Kiliaridis S, Kalebo P: Masseter muscle thickness measured by ultrasonography and its relation to facial morphology. J Dent Res 1991; 70: 12621265.10.1177/002203459107000906011918575Search in Google Scholar

Close PJ, Stokes MJ, L’Estrange PR, Rowell J: Ultrasonography of masseter muscle size in normal young adults. J Oral Rehabil 1995; 22: 129–134. Close PJ, Stokes MJ, L’Estrange PR, Rowell J: Ultrasonography of masseter muscle size in normal young adults. J Oral Rehabil 1995; 22: 129134.10.1111/j.1365-2842.1995.tb00246.x7722744Search in Google Scholar

Raadsheer MC, Van Eijden TM, Van Ginkel FC, Prahl-Andersen B: Human jaw muscle strength and size in relation to limb muscle nstrength and size. Eur J Oral Sci 2004; 112: 398–405. Raadsheer MC, Van Eijden TM, Van Ginkel FC, Prahl-Andersen B: Human jaw muscle strength and size in relation to limb muscle nstrength and size. Eur J Oral Sci 2004; 112: 398405.10.1111/j.1600-0722.2004.00154.x15458497Search in Google Scholar

Jonasson G, Kiliaridis S: The association between the masseter muscle, the mandibular alveolar bone mass and thickness in dentate women. Arch Oral Biol 2004; 49: 1001–1006. Jonasson G, Kiliaridis S: The association between the masseter muscle, the mandibular alveolar bone mass and thickness in dentate women. Arch Oral Biol 2004; 49: 10011006.10.1016/j.archoralbio.2004.07.00515485642Search in Google Scholar

Emshoff R, Emshoff I, Rudisch A, Bertram S: Reliability and temporal variation of masseter muscle thickness measurements utilizing ultrasonography. J Oral Rehabil 2003; 30: 1168–1172. Emshoff R, Emshoff I, Rudisch A, Bertram S: Reliability and temporal variation of masseter muscle thickness measurements utilizing ultrasonography. J Oral Rehabil 2003; 30: 11681172.10.1111/j.1365-2842.2003.01186.x14641658Search in Google Scholar

Satiroğlu F, Arun T, Işik F: Comparative data on facial morphology and muscle thickness using ultrasonography. Eur J Orthod 2005; 27: 562–567. Satiroğlu F, Arun T, Işik F: Comparative data on facial morphology and muscle thickness using ultrasonography. Eur J Orthod 2005; 27: 562567.10.1093/ejo/cji05216135538Search in Google Scholar

Egwu OA, Njoku CO, Ewunonu EO, Ukoha U, Eteudo AN, Mgbachi CE: Assessment of masseter muscle thickness in an adult Nigerian population: an ultrasound based study. Int J Biomed Res 2012; 3: 143–146. Egwu OA, Njoku CO, Ewunonu EO, Ukoha U, Eteudo AN, Mgbachi CE: Assessment of masseter muscle thickness in an adult Nigerian population: an ultrasound based study. Int J Biomed Res 2012; 3: 143146.10.7439/ijbr.v3i3.304Search in Google Scholar

Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH: Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res 2019; 21: e12422. Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH: Physician confidence in artificial intelligence: an online mobile survey. J Med Internet Res 2019; 21: e12422.10.2196/12422645228830907742Search in Google Scholar

Dreyer KJ, Geis JR: When machines think: radiology’s next frontier. Radiology 2017; 285: 713–718. Dreyer KJ, Geis JR: When machines think: radiology’s next frontier. Radiology 2017; 285: 713718.10.1148/radiol.201717118329155639Search in Google Scholar

Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A: Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: a survey. Imaging Sci Dent 2020; 50: 193–198. Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A: Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: a survey. Imaging Sci Dent 2020; 50: 193198.10.5624/isd.2020.50.3.193750609133005576Search in Google Scholar

Alsharqi M, Woodward WJ, Mumith JA, Markham DC, Upton R, Leeson P: Artificial intelligence and echocardiography. Echo Res Pract 2018; 5: R115–R125. Alsharqi M, Woodward WJ, Mumith JA, Markham DC, Upton R, Leeson P: Artificial intelligence and echocardiography. Echo Res Pract 2018; 5: R115R125.10.1530/ERP-18-0056628025030400053Search in Google Scholar

Hwang J-J, Jung Y-H, Cho B-H, Heo M-S: An overview of deep learning in the field of dentistry. Imaging Sci Dent 2019; 49: 1–7. Hwang J-J, Jung Y-H, Cho B-H, Heo M-S: An overview of deep learning in the field of dentistry. Imaging Sci Dent 2019; 49: 17.10.5624/isd.2019.49.1.1644400730941282Search in Google Scholar

Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M: Use of artificial neural network in differentiation of subgroupsof temporomandibular internal derangements: a preliminary study. J Oral Maxillofac Surg 2012; 70: 51–59. Bas B, Ozgonenel O, Ozden B, Bekcioglu B, Bulut E, Kurt M: Use of artificial neural network in differentiation of subgroupsof temporomandibular internal derangements: a preliminary study. J Oral Maxillofac Surg 2012; 70: 5159.10.1016/j.joms.2011.03.06921802818Search in Google Scholar

Keser G, Namdar Pekiner F: Attitudes, perceptions and knowledge regarding the future of artificial intelligence in oral radiology among a group of dental students in Turkey: a survey. Clin Exp Health Sci 2021; 11: 637–641. Keser G, Namdar Pekiner F: Attitudes, perceptions and knowledge regarding the future of artificial intelligence in oral radiology among a group of dental students in Turkey: a survey. Clin Exp Health Sci 2021; 11: 637641.10.33808/clinexphealthsci.928246Search in Google Scholar

Lee J-H, Kim D-H, Jeong S-N, Choi S-H: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018; 77: 106–111. Lee J-H, Kim D-H, Jeong S-N, Choi S-H: Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018; 77: 106111.10.1016/j.jdent.2018.07.01530056118Search in Google Scholar

Poedjiastoeti W, Suebnukarn S: Application of convolutional neural network in the diagnosis of jaw tumors. Health Inform Res 2018; 24: 236–241. Poedjiastoeti W, Suebnukarn S: Application of convolutional neural network in the diagnosis of jaw tumors. Health Inform Res 2018; 24: 236241.10.4258/hir.2018.24.3.236608520830109156Search in Google Scholar

Faber J, Faber C, Faber P: Artificial intelligence in orthodontics. APOS Trends Orthod 2019; 9: 201–205. Faber J, Faber C, Faber P: Artificial intelligence in orthodontics. APOS Trends Orthod 2019; 9: 201205.10.25259/APOS_123_2019Search in Google Scholar

Woo S-Y, Lee S-J, Yoo J-Y, Han J-J, Hwang S-J, Huh K-H et al.: Autonomous bone reposition around anatomical landmark for robotassisted orthognathic surgery. J Craniomaxillofac Surg 2017; 45: 1980–1988. Woo S-Y, Lee S-J, Yoo J-Y, Han J-J, Hwang S-J, Huh K-H et al.: Autonomous bone reposition around anatomical landmark for robotassisted orthognathic surgery. J Craniomaxillofac Surg 2017; 45: 19801988.10.1016/j.jcms.2017.09.00129042168Search in Google Scholar

Shin Y, Yang J, Lee YH, Kim S: Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography 2021; 4: 30–44. Shin Y, Yang J, Lee YH, Kim S: Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography 2021; 4: 3044.10.14366/usg.20080775809633242932Search in Google Scholar

Ronneberger O, Fischer P, Brox T: U-net: convolutional networks for biomedical image segmentation. Springer 2015: 234–241. Ronneberger O, Fischer P, Brox T: U-net: convolutional networks for biomedical image segmentation. Springer 2015: 234241.10.1007/978-3-319-24574-4_28Search in Google Scholar

Shelhamer E, Long L, Darrell T: Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39: 640–651. Shelhamer E, Long L, Darrell T: Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39: 640651.10.1109/TPAMI.2016.257268327244717Search in Google Scholar

Ronneberger O, Fischer P, Brox T: U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds): Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer, Cham 2015: 9351. Ronneberger O, Fischer P, Brox T: U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds): Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Springer, Cham 2015: 9351.10.1007/978-3-319-24574-4_28Search in Google Scholar

Nwawka OK: Update in musculoskeletal ultrasound research. Sports Health 2016; 8: 429–437. Nwawka OK: Update in musculoskeletal ultrasound research. Sports Health 2016; 8: 429437.10.1177/1941738116664326501014027528698Search in Google Scholar

Powers J, Kremkau F: Medical ultrasound systems. Interface Focus 2011; 1: 477–489. Powers J, Kremkau F: Medical ultrasound systems. Interface Focus 2011; 1: 477489.10.1098/rsfs.2011.0027326227522866226Search in Google Scholar

Liu F, Zhou Z, Samsonov A, Blankenbaker D, Larison W, Kanarek A et al.: Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology 2018; 289: 160–169. Liu F, Zhou Z, Samsonov A, Blankenbaker D, Larison W, Kanarek A et al.: Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology 2018; 289: 160169.10.1148/radiol.2018172986616686730063195Search in Google Scholar

Lakhani P, Sundaram B: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017; 284: 574–582. Lakhani P, Sundaram B: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017; 284: 574582.10.1148/radiol.201716232628436741Search in Google Scholar

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115–118. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115118.10.1038/nature21056838223228117445Search in Google Scholar

Arcadu F, Benmansour F, Maunz A, Willis J, Haskova Z, Prunotto M: Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med 2019; 2: 92. Arcadu F, Benmansour F, Maunz A, Willis J, Haskova Z, Prunotto M: Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med 2019; 2: 92.10.1038/s41746-019-0172-3675445131552296Search in Google Scholar

Orhan K, Yazici G, Kolsuz ME, Kafa N, Bayrakdar IS, Çelik Ö: An artificial intelligence hypothetical approach for masseter muscle segmentation on ultrasonography in patients with bruxism. J Adv Oral Res 2021; 12: 206–213. Orhan K, Yazici G, Kolsuz ME, Kafa N, Bayrakdar IS, Çelik Ö: An artificial intelligence hypothetical approach for masseter muscle segmentation on ultrasonography in patients with bruxism. J Adv Oral Res 2021; 12: 206213.10.1177/23202068211005611Search in Google Scholar

Prevost R, Salehi M, Jagoda S, Kumar N, Sprung J, Ladikos A et al.: 3D freehand ultrasound without external tracking using deep learning. Med Image Anal 2018; 48: 187–202. Prevost R, Salehi M, Jagoda S, Kumar N, Sprung J, Ladikos A et al.: 3D freehand ultrasound without external tracking using deep learning. Med Image Anal 2018; 48: 187202.10.1016/j.media.2018.06.00329936399Search in Google Scholar

Looney P, Stevenson GN, Nicolaides KH, Plasencia W, Molloholli M, Natsis S et al.: Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. JCI Insight 2018; 3: e120178. Looney P, Stevenson GN, Nicolaides KH, Plasencia W, Molloholli M, Natsis S et al.: Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. JCI Insight 2018; 3: e120178.10.1172/jci.insight.120178612443629875312Search in Google Scholar

eISSN:
2451-070X
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Medicine, Basic Medical Science, other