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Research Article

Deep learning in oral surgery for third molar extraction: empirical evidence and original model

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Article: 2349564 | Received 09 Nov 2023, Accepted 25 Apr 2024, Published online: 12 May 2024

References

  • Wang R, Wang S, Duan N, et al. From patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: practices and perspectives. Front Med. 2020;7:145. doi: 10.3389/fmed.2020.00145.
  • AbuSalim S, Zakaria N, Islam MR, et al. Analysis of deep learning techniques for dental informatics: a systematic literature review. Healthcare. 2022;10(10):1892. doi: 10.3390/healthcare10101892.
  • Werner P, Lopez-Martinez D, Walter S, et al. Automatic recognition methods supporting pain assessment: a survey. IEEE Trans Affective Comput. 2022;13(1):530–552. doi: 10.1109/TAFFC.2019.2946774.
  • Aida S, Baba H, Yamakura T, et al. The effectiveness of preemptive analgesia varies according to the type of surgery: a randomized, double-blind study. Anesth Analg. 1999;89(3):711–716. doi: 10.1097/00000539-199909000-00034.
  • Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. AIMI. 2021;2(6):104–114. doi: 10.35711/aimi.v2.i6.104.
  • Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics. 2022;12(4):942. doi: 10.3390/diagnostics12040942.
  • Yang S, Zhu F, Ling X, et al. Intelligent health care: applications of deep learning in computational medicine. Front Genet. 2021;12:607471. doi: 10.3389/fgene.2021.607471.
  • Alhazmi A, Alhazmi Y, Makrami A, et al. Application of artificial intelligence and machine learning for prediction of oral cancer risk. J Oral Pathol Med. 2021;50(5):444–450. doi: 10.1111/jop.13157.
  • Yoo JH, Yeom HG, Shin W, et al. Deep learning based prediction of extraction difficulty for mandibular third molars. Sci Rep. 2021;11(1):1954. doi: 10.1038/s41598-021-81449-4.
  • Cascella M, Schiavo D, Cuomo A, et al. Artificial intelligence for automatic pain assessment: research methods and perspectives. Pain Res Manag. 2023;2023:6018736–6018713. doi: 10.1155/2023/6018736.
  • Cui Q, Chen Q, Liu P, et al. Clinical decision support model for tooth extraction therapy derived from electronic dental records. J Prosthet Dent. 2021;126(1):83–90. doi: 10.1016/j.prosdent.2020.04.010.
  • Kim BS, Yeom HG, Lee JH, et al. Deep learning-based prediction of paresthesia after third molar extraction: a preliminary study. Diagnostics. 2021;11(9):1572. doi: 10.3390/diagnostics11091572.
  • Kang IA, Ngnamsie Njimbouom S, Lee KO, et al. DCP: prediction of dental caries using machine learning in personalized medicine. Appl Sci. 2022;12(6):3043. doi: 10.3390/app12063043.
  • Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: The MIT Press; 2016.
  • He A, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. IEEE International Conference on Computer Vision (ICCV), 7-13 Dec. 2015, Santiago, Chile. IEEE Computer Society. p. 1026–1034.
  • Bishop CM. Neural networks for pattern recognition. Oxford: Department of Computer Science and Applied Mathematics Aston University, Clarendon Press Oxford; 1995.
  • Murphy K. Machine learning: a probabilistic perspective. Cambridge: MIT; 2012.
  • Barry J. A deep learning approach to diagnosing schizophrenia. Orlando FL: University of Central Florida; 2019.
  • Tenglikar P, Munnangi A, Mangalgi A, et al. An assessment of factors influencing the difficulty in third molar surgery. Ann Maxillofac Surg. 2017;7(1):45–50. doi: 10.4103/ams.ams_194_15.
  • Lago-Méndez L, Diniz-Freitas M, Senra-Rivera C, et al. Relationships between surgical difficulty and postoperative pain in lower third molar extractions. J Oral Maxillofac Surg. 2007;65(5):979–983. doi: 10.1016/j.joms.2006.06.281.
  • Barreiro-Torres J, Diniz-Freitas M, Lago-Méndez L, et al. Evaluation of the surgical difficulty in lower third molar extraction. Med Oral Patol Oral Cir Bucal. 2010;15(6):e869–e874. doi: 10.4317/medoral.15.e869.
  • Lötsch J, Ultsch A. Machine learning in pain research. Pain. 2018;159(4):623–630. doi: 10.1097/j.pain.0000000000001118.