Three dimensional biometric guide in determining maxillary tooth position and arch form / Omar Farouq Tawfiq - Omar Farouq, Tawfiq (2020)
In complete denture fabrication, when pre-extraction records are lacking, determining the denture teeth positions to where the natural predecessors occupied is essential yet challenging. Although several biometric guides were suggested to position teeth for dentures, none is providing three-dimensional (3D) positions of teeth that can be used in the recently developed computer designed digital dentures. The aim of this study was to develop new biometric guides to determine the original 3D positions of the maxillary teeth and dental arch form individualised for each given dentate or edentulous maxillary cast based on selected intraoral landmarks. The objectives were to investigate the relationship between measurements of selected maxillary anatomical landmarks, to verify the accuracy of the relationship equations in predicting tooth position; to predict teeth positions for edentulous casts; to investigate the dental-arch curves morphological relationship with HNR-curve and predictability of dental arch for dentulous casts from HNR-curve. Ninety-two Malaysian adults (20-35 years old) who had class I dental and skeletal relationships, well-aligned teeth and minimal attritions were selected for the study. Maxillary stone-casts were obtained, digitised and standardised. The points’ 3D Cartesian-coordinates were converted into sphericalcoordinates for statistical analyses. The dentate sample (n=92) was subdivided randomly into control group (n=70) and dentate verification group (n=22). The iv control group was used to investigate linear regressions and functional circular relationships to generate equations, while the verification group was used to compare the coordinates of teeth predicted by the relationship equations with the measured coordinates in each cast of the group using paired t-test. Dental arch curves and hamular-notch rugae-point curve (HNR-curve) were fitted to polynomial-fourth-degree equations and compared for shape-similarity using Z-test (α = 0.05). Then artificial neural network (ANN) was used to generate dental arch curves by HNR-curves for dentate casts, verify the prediction accuracy of the method before application on edentulous casts. Thirty-four maxillary edentulous casts were obtained, digitised, standardised and had the triangular pyramid landmarks coordinates similar to the dentate casts registered and used to predict teeth positions. The results showed high correlation coefficients between the landmarks and teeth positions (0.5 ≤ r ≤ 0.9, p < 0.05). Fifty-four regression and circular relationship equations were derived to predict the teeth positions (0.89 ≤ R 2 ≤ 0.998). No significant differences were found between the existing and predicted coordinates of teeth through the verification group subjects (p > 0.05). When the arch forms were compared for similarity with HNR-curve; Z-test values (SD) were 0.894(0.64), 0.705(0.51), 0.382(0.31) for buccal, middle and lingual dental arches respectively. Within the limitations of this study, predicted teeth positions showed non-significant difference with the natural teeth positions when verified in new dentate sample. Furthermore, predicted teeth positions for edentulous sample showed statistical equivalence with the range of the natural teeth positions in dentate sample. Additionally, the predicted dental arch forms for verification dentate group showed no significant difference with the original dental arches. Conclusively, the maxillary teeth 3D-points together with the dental arch form may suggest v acceptable guide for digital dental rehabilitation for edentulous patients using the latest digital denture manufacturing technology based on stable intraoral anatomical landmarks with strong coefficients. Keywords: Complete denture, Biometric guides; dental arch form; 3D tooth position; CAD/CAM complete denture.