Takafumi Otani1, Ariel J Raigrodski2, Lloyd Mancl3, Ikuru Kanuma4, Jacob Rosen5. 1. Graduate student, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash. Electronic address: takadent@u.washington.edu. 2. Professor, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash. 3. Research Associate Professor, Department of Oral Health Sciences, School of Dentistry, University of Washington, Seattle, Wash. 4. Student, Department of Computer Engineering, University of California, Santa Cruz, Calif. 5. Associate Professor, Department of Computer Engineering, University of California, Santa Cruz, Calif.
Abstract
STATEMENT OF PROBLEM: Controlling tooth reduction for porcelain laminate veneers (PLVs) in fractions of millimeters is challenging. PURPOSE: The purpose of this study was to assess an automated robotic tooth preparation system for PLVs for accuracy and precision compared with conventional freehand tooth preparation. MATERIAL AND METHODS: Twenty maxillary central incisor tooth models were divided into 2 groups. Ten were assigned to a veneer preparation with a robotic arm according to preoperative preparation design-specific guidelines (experimental group). Ten were assigned to conventional tooth preparation by a clinician (control group). Initially, all tooth models were scanned with a 3- dimensional (3D) laser scanner, and a tooth preparation for PLVs was designed on a 3D image. Each tooth model was attached to a typodont. For the experimental group, an electric high-speed handpiece with a 0.9-mm-diameter round diamond rotary cutting instrument was mounted on the robotic arm. The teeth were prepared automatically according to the designed image. For the control group, several diamond rotary cutting instruments were used to prepare the tooth models according to preoperative preparation design guidelines. All prepared tooth models were scanned. The preoperative preparation design image and scanned postoperative preparation images were superimposed. The dimensional difference between those 2 images was measured on the facial aspect, finish line, and incisal edge. Differences between the experimental and the control groups from the 3D design image were computed. Accuracy and precision were compared for all sites and separately for each tooth surface (facial, finish line, incisal). Statistical analyses were conducted with a permutation test for accuracy and with a modified robust Brown-Forsythe Levene-type test for precision (α=.05). RESULTS: For accuracy for all sites, the mean absolute deviation was 0.112 mm in the control group and 0.133 mm in the experimental group. No significant difference was found between the 2 (P=.15). For precision of all sites, the standard deviation was 0.141 mm in the control group and 0.185 mm in the experimental group. The standard deviation in the control group was significantly lower (P=.030). In terms of accuracy for the finish line, the control group was significantly less accurate (P=.038). For precision, the standard deviation in the control group was significantly higher at the finish line (P=.034). CONCLUSIONS: For the data from all sites, the experimental procedure was able to prepare the tooth model as accurately as the control, and the control procedure was able to prepare the tooth model with better precision. The experimental group showed better accuracy and precision at the finish line.
STATEMENT OF PROBLEM: Controlling tooth reduction for porcelain laminate veneers (PLVs) in fractions of millimeters is challenging. PURPOSE: The purpose of this study was to assess an automated robotic tooth preparation system for PLVs for accuracy and precision compared with conventional freehand tooth preparation. MATERIAL AND METHODS: Twenty maxillary central incisor tooth models were divided into 2 groups. Ten were assigned to a veneer preparation with a robotic arm according to preoperative preparation design-specific guidelines (experimental group). Ten were assigned to conventional tooth preparation by a clinician (control group). Initially, all tooth models were scanned with a 3- dimensional (3D) laser scanner, and a tooth preparation for PLVs was designed on a 3D image. Each tooth model was attached to a typodont. For the experimental group, an electric high-speed handpiece with a 0.9-mm-diameter round diamond rotary cutting instrument was mounted on the robotic arm. The teeth were prepared automatically according to the designed image. For the control group, several diamond rotary cutting instruments were used to prepare the tooth models according to preoperative preparation design guidelines. All prepared tooth models were scanned. The preoperative preparation design image and scanned postoperative preparation images were superimposed. The dimensional difference between those 2 images was measured on the facial aspect, finish line, and incisal edge. Differences between the experimental and the control groups from the 3D design image were computed. Accuracy and precision were compared for all sites and separately for each tooth surface (facial, finish line, incisal). Statistical analyses were conducted with a permutation test for accuracy and with a modified robust Brown-Forsythe Levene-type test for precision (α=.05). RESULTS: For accuracy for all sites, the mean absolute deviation was 0.112 mm in the control group and 0.133 mm in the experimental group. No significant difference was found between the 2 (P=.15). For precision of all sites, the standard deviation was 0.141 mm in the control group and 0.185 mm in the experimental group. The standard deviation in the control group was significantly lower (P=.030). In terms of accuracy for the finish line, the control group was significantly less accurate (P=.038). For precision, the standard deviation in the control group was significantly higher at the finish line (P=.034). CONCLUSIONS: For the data from all sites, the experimental procedure was able to prepare the tooth model as accurately as the control, and the control procedure was able to prepare the tooth model with better precision. The experimental group showed better accuracy and precision at the finish line.
Authors: Paras Ahmad; Mohammad Khursheed Alam; Ali Aldajani; Abdulmajeed Alahmari; Amal Alanazi; Martin Stoddart; Mohammed G Sghaireen Journal: Sensors (Basel) Date: 2021-05-11 Impact factor: 3.576
Authors: Naseer Ahmed; Maria Shakoor Abbasi; Filza Zuberi; Warisha Qamar; Mohamad Syahrizal Bin Halim; Afsheen Maqsood; Mohammad Khursheed Alam Journal: Biomed Res Int Date: 2021-06-22 Impact factor: 3.411