Janina Golob Deeb1, Sompop Bencharit2,3, Caroline K Carrico4, Marija Lukic5, Daniel Hawkins6, Ksenija Rener-Sitar5,7, George R Deeb6. 1. Department of Periodontics, School of Dentistry, Virginia Commonwealth University, Richmond, Virginia, USA. 2. Department of General Practice, School of Dentistry, Virginia Commonwealth University, Richmond, Virginia, USA. 3. Department of Biomedical Engineering, School of Engineering, Virginia Commonwealth University, Richmond, Virginia, USA. 4. Oral Health Promotion and Community Outreach, Oral Health Research Core, Virginia Commonwealth University, Richmond, Virginia, USA. 5. Division for Dental Medicine, Medical Faculty, University of Ljubljana, Ljubljana, Slovenia. 6. School of Dentistry, Department of Oral and Maxillofacial Surgery, Virginia Commonwealth University, Richmond, Virginia, USA. 7. Department of Prosthodontics, University Dental Clinics, University Medical Centre of Ljubljana, Ljubljana, Slovenia.
Abstract
INTRODUCTION: Recent computer-guided dynamic navigation systems promise a novel training approach for implant surgery. This study aimed to examine learning progress in placement of dental implants among dental students using dynamic navigation on a simulation model. MATERIALS AND METHODS: Senior students with no implant placement experience were randomly assigned five implant placement attempts involving either three maxillary or four mandibular implants distributed in the anterior/posterior, and left/right segments. Implant placement was planned using a Navident Dynamic Guidance system. Surgical time was recorded. Horizontal, vertical and angulation discrepancies between the planned and placed implant positions were measured using superimposed CBCT scans. Data were analysed with repeated measures regression with Tukey's adjusted pairwise comparisons (α = 0.05). RESULTS: Fourteen students participated, with a mean age of 26.1 years and equal males and females. Mean time for implant placement was associated with attempt number (P < 0.001), implant site (P = 0.010) and marginally related to gender (P = 0.061). Students had a significant reduction in time from their first attempt to their second (10.6 vs 7.6 minutes; adjusted P < 0.001) then plateaued. Overall 3D angulation (P < 0.001) and 2D vertical apex deviation (P = 0.014) improved with each attempt, but changes in lateral 2D (P = 0.513) and overall 3D apex deviations (P = 0.784) were not statistically significant. Implant sites were associated with lateral 2D, 2D vertical and overall 3D apex deviation (P < 0.001). DISCUSSION: Males were marginally faster than females, had slightly lower overall 3D angulation, and reported higher proficiency with video games. Novice operators improved significantly in speed and angulation deviation within the first three attempts of placing implants using dynamic navigation. CONCLUSION: Computer-aided dynamic implant navigation systems can improve implant surgical training in novice population.
INTRODUCTION: Recent computer-guided dynamic navigation systems promise a novel training approach for implant surgery. This study aimed to examine learning progress in placement of dental implants among dental students using dynamic navigation on a simulation model. MATERIALS AND METHODS: Senior students with no implant placement experience were randomly assigned five implant placement attempts involving either three maxillary or four mandibular implants distributed in the anterior/posterior, and left/right segments. Implant placement was planned using a Navident Dynamic Guidance system. Surgical time was recorded. Horizontal, vertical and angulation discrepancies between the planned and placed implant positions were measured using superimposed CBCT scans. Data were analysed with repeated measures regression with Tukey's adjusted pairwise comparisons (α = 0.05). RESULTS: Fourteen students participated, with a mean age of 26.1 years and equal males and females. Mean time for implant placement was associated with attempt number (P < 0.001), implant site (P = 0.010) and marginally related to gender (P = 0.061). Students had a significant reduction in time from their first attempt to their second (10.6 vs 7.6 minutes; adjusted P < 0.001) then plateaued. Overall 3D angulation (P < 0.001) and 2D vertical apex deviation (P = 0.014) improved with each attempt, but changes in lateral 2D (P = 0.513) and overall 3D apex deviations (P = 0.784) were not statistically significant. Implant sites were associated with lateral 2D, 2D vertical and overall 3D apex deviation (P < 0.001). DISCUSSION: Males were marginally faster than females, had slightly lower overall 3D angulation, and reported higher proficiency with video games. Novice operators improved significantly in speed and angulation deviation within the first three attempts of placing implants using dynamic navigation. CONCLUSION: Computer-aided dynamic implant navigation systems can improve implant surgical training in novice population.
Authors: Miguel Pais Clemente; André Moreira; João Correia Pinto; José Manuel Amarante; Joaquim Mendes Journal: Inquiry Date: 2021 Jan-Dec Impact factor: 1.730
Authors: Xiaotong Wang; Eman Shaheen; Sohaib Shujaat; Jan Meeus; Paul Legrand; Pierre Lahoud; Maurício do Nascimento Gerhardt; Constantinus Politis; Reinhilde Jacobs Journal: Int J Implant Dent Date: 2022-10-10
Authors: Francesco Grecchi; Luigi V Stefanelli; Fabrizio Grivetto; Emma Grecchi; Rami Siev; Ziv Mazor; Massimo Del Fabbro; Nicola Pranno; Alessio Franchina; Vittorio Di Lucia; Francesca De Angelis; Funda Goker Journal: Int J Environ Res Public Health Date: 2021-06-07 Impact factor: 3.390