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The accuracy and learning curve of active and passive dynamic navigation-guided dental implant surgery: An in vitro study.
Wang, Xiao-Yu; Liu, Lin; Guan, Miao-Sheng; Liu, Qian; Zhao, Tong; Li, Hong-Bo.
Afiliación
  • Wang XY; Department of Stomatology, The First Medical Center, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, China; Department of Stomatology, The Strategic Support Force Medical Center, PLA, Beijing, China.
  • Liu L; Department of Stomatology, The First Medical Center, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, China.
  • Guan MS; Department of Stomatology, The First Medical Center, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, China; Department of Research, PLA Rocket Force Characteristic Medical Center, PLA, Beijing, China.
  • Liu Q; Department of Stomatology, The First Medical Center, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, China.
  • Zhao T; School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
  • Li HB; Department of Stomatology, The First Medical Center, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, China. Electronic address: hongbo_l@sina.com.
J Dent ; 124: 104240, 2022 09.
Article en En | MEDLINE | ID: mdl-35872224
OBJECTIVES: Infrared dynamic navigation systems can be categorized into active and passive based on whether the surgical instruments can emit or only reflect light. This in vitro study aimed to compare the accuracy of implant placement and the learning curve of both active and passive dynamic navigation systems, using different registration methods. METHODS: Implants (n = 704) were placed in 64 sets of models and divided into active (Yizhime, DCARER, Suzhou, China) and passive (Iris-Clinic, EPED, Kaohsiung, China) dynamic navigation groups. Both marker point-based registration (M-PBR) and feature point-based registration (F-PBR) were employed for the two groups. Based on preoperative and postoperative cone-beam computed tomography imaging, the coronal, midpoint, apical, and angular deviations were analyzed from 2D and 3D views. The operation time was recorded for each group. RESULTS: The active dynamic navigation group exhibited significantly higher accuracy than the passive dynamic navigation group (angular deviation, 4.13 ± 2.39° versus 4.62 ± 3.32°; coronal global deviation, 1.48 ± 0.60 versus 1.86 ± 1.12 mm; apical global deviation, 1.75 ± 0.81 versus 2.20 ± 1.68 mm, respectively). Significant interaction effects were observed for both registration methods and four quadrants with different dynamic navigation systems. Learning curves for the two dynamic navigation groups approached each other after 12 procedures, and finally converged after 27 procedures. CONCLUSIONS: The accuracy of active dynamic navigation system was superior to that of passive dynamic navigation system. Different combinations of dynamic navigation systems, registration methods, and implanted quadrants displayed various interactions. CLINICAL SIGNIFICANCE: Our findings could provide guidance for surgeons in choosing an appropriate navigation system in various implant surgeries. Furthermore, the time required by surgeons to master the technique was calculated. Nevertheless, there are certain limitations in this in vitro study, and therefore further research is required.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Implantes Dentales / Cirugía Asistida por Computador Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: J Dent Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Implantes Dentales / Cirugía Asistida por Computador Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: J Dent Año: 2022 Tipo del documento: Article País de afiliación: China