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Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs.
Okazaki, Shota; Mine, Yuichi; Iwamoto, Yuko; Urabe, Shiho; Mitsuhata, Chieko; Nomura, Ryota; Kakimoto, Naoya; Murayama, Takeshi.
Affiliation
  • Okazaki S; Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Mine Y; Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Iwamoto Y; Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Urabe S; Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Mitsuhata C; Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Nomura R; Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Kakimoto N; Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University.
  • Murayama T; Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University.
Dent Mater J ; 41(6): 889-895, 2022 Nov 30.
Article in En | MEDLINE | ID: mdl-36002296
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Tooth, Supernumerary / Odontoma / Deep Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Dent Mater J Year: 2022 Document type: Article Country of publication: Japón
Fulltext
Add to My VHL
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Tooth, Supernumerary / Odontoma / Deep Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Dent Mater J Year: 2022 Document type: Article Country of publication: Japón