A Retrospective Study on the Transferring Accuracy of a Fully Guided Digital Template in the Anterior Zone

The accuracy of implant placement with a fully guided digital template can be influenced by many factors, such as arch difference, alveolar bone density, timing of implant placement and open flap. The purpose of this article was to evaluate the factors presumptively affecting the accuracy of implant placement assisted by the fully guided template in the anterior zone. In 40 patients with missing anterior teeth, a total of 52 implants were placed with tooth-borne, fully guided templates after CBCT evaluation, in West China Hospital of Stomatology, Sichuan University. After overlapping the pre-and post-operative DICOM data, measurements were taken in the dental implant planning software (Nobel Clinician®) to calculate linear and angular deviations between virtual placement plan and actual implant placement. Grouping was categorized according to three factors that possibly have an influence on accuracy: arch type (maxilla/mandible), timing of implant placement (immediate/delayed), surgical technique (open flap/flapless). The data was analyzed with independent sample t-test (p < 0.05). The results showed that the apical, coronal, depth and angular mean deviations of implant positions in anterior zone were 1.13 ± 0.39 mm, 0.86 ± 0.33 mm, 0.41 ± 0.66 mm, 3.32 ± 1.65° with the fully guided templates. The accuracy at apex level, coronal level and the angulation were similar between the maxilla and mandible, and the magnitude of all four deviations between immediate and delayed implantation, open flap and flapless technique were small. No statistically significant difference was observed (p > 0.05). Whereas there was significant difference in depth deviation between maxilla and mandible (p < 0.05). Conclusively, the implant site, alveolar bone density, timing of implant placement and surgical techniques merely compromise the implant placement accuracy under the assistance of a fully guided template.

Value of 3D preoperative planning for primary total hip arthroplasty based on artificial intelligence technology

Background Accurate preoperative planning is an important step for accurate reconstruction in total hip arthroplasty (THA). Presently, preoperative planning is completed using either a two-dimensional (2D) template or three-dimensional (3D) mimics software. With the development of artificial intelligence (AI) technology, AI HIP, a planning software based on AI technology, can quickly and automatically identify acetabular and femur morphology, and automatically match the optimal prosthesis size. However, the accuracy and feasibility of its clinical application still needs to be further verified. The purposes of this study were to investigate the accuracy and time efficiency of AI HIP in preoperative planning for primary THA, compared with 3D mimics software and 2D digital template, and further analyze the factors that influence the accuracy of AI HIP. Methods A prospective study was conducted on 53 consecutive patients (59 hips) undergoing primary THA with cementless prostheses in our department. All preoperative planning was completed using AI HIP as well as 3D mimics and 2D digital template. The predicted component size and the actual implantation results were compared to determine the accuracy. The templating time was compared to determine the efficiency. Furthermore, the potential factors influencing the accuracy of AI HIP were analyzed including sex, body mass index (BMI), and hip dysplasia. Results The accuracy of predicting the size of acetabular cup and femoral stem was 74.58% and 71.19%, respectively, for AI HIP; 71.19% (P = 0.743) and 76.27% (P = 0.468), respectively, for 3D mimics; and 40.68% (P < 0.001) and 49.15% (P = 0.021), respectively, for 2D digital templating. The templating time using AI HIP was 3.91 ± 0.64 min, which was equivalent to 2D digital templates (2.96 ± 0.48 min, P < 0.001), but shorter than 3D mimics (32.07 ± 2.41 min, P < 0.001). Acetabular dysplasia (P = 0.021), rather than sex and BMI, was an influential factor in the accuracy of AI HIP templating. Compared to patients with developmental dysplasia of the hip (DDH), the accuracy of acetabular cup in the non-DDH group was better (P = 0.021), but the difference in the accuracy of the femoral stem between the two groups was statistically insignificant (P = 0.062). Conclusion AI HIP showed excellent reliability for component size in THA. Acetabular dysplasia may affect the accuracy of AI HIP templating.

Value of 3D Preoperative Planning for Primary Total Hip Arthroplasty Based on Artificial Intelligence Technology

Background: Accurate preoperative planning is an important step for accurate reconstruction in total hip arthroplasty (THA). Presently, preoperative planning is completed using either a two-dimensional (2D) template or three-dimensional (3D) mimics software. With the development of artificial intelligence (AI) technology, AI HIP, a planning software based on AI technology can quickly and automatically identify acetabular and femur morphology, and automatically match the optimal prosthesis size. However, the accuracy and feasibility of its clinical application still needs to be further verified. The purposes of this study were to investigate the accuracy and time efficiency of AI HIP in preoperative planning for primary THA, compared with 3D mimics software and 2D digital template; and further analyze the factors that influence the accuracy of AI HIP.Methods: A prospective study was conducted on 53 consecutive patients (59 hips) undergoing primary THA with cementless prostheses in our department. All preoperative planning was completed using AI HIP as well as 3D mimics and 2D digital template. The predicted component size and the actual implantation results were compared to determine the accuracy. The templating time was compared to determine the efficiency. Furthermore, the potential factors influencing the accuracy of AI HIP were analyzed including sex, body mass index (BMI), and hip dysplasia.Results: The accuracy of predicting the size of acetabular cup and femoral stem was 74.58% and 71.19%, respectively, for AI HIP; 71.19% (P = 0.743) and 76.27% (P = 0.468), respectively, for 3D mimics; 40.68% (P < 0.001) and 49.15% (P = 0.021), respectively, for 2D digital templating. The templating time using AI HIP was 3.91±0.64 min, which was equivalent to 2D digital templates (2.96±0.48 min, P < 0.001), but shorter than 3D mimics (32.07±2.41 min, P < 0.001). Acetabular dysplasia(P = 0.021), rather than sex and BMI, was an influential factor in the accuracy of AI HIP templating. Compared to patients with developmental dysplasia of the hip (DDH), the accuracy of acetabular cup in the non-DDH group was better (P = 0.021), but the difference in the accuracy of the femoral stem between the two groups was statistically insignificant (P = 0.062).Conclusion: AI HIP showed excellent reliability for component size in THA. Acetabular dysplasia may affect the accuracy of AI HIP templating.

Value of 3D Preoperative Planning for Primary Total Hip Arthroplasty Based on Artificial Intelligence Technology

Background: Accurate preoperative planning is an important step for accurate reconstruction in total hip arthroplasty (THA). Presently, preoperative planning is completed using either a two-dimensional (2D) template or three-dimensional (3D) mimics software. With the development of artificial intelligence (AI) technology, AI HIP, a planning software based on AI technology can quickly and automatically identify acetabular and femur morphology, and automatically match the optimal prosthesis size. However, the accuracy and feasibility of its clinical application still needs to be further verified. The purposes of this study were to investigate the accuracy and time efficiency of AI HIP in preoperative planning for primary THA, compared with 3D mimics software and 2D digital template; and further analyze the factors that influence the accuracy of AI HIP.Methods: A prospective study was conducted on 53 consecutive patients (59 hips) undergoing primary THA with cementless prostheses in our department. All preoperative planning was completed using AI HIP as well as 3D mimics and 2D digital template. The predicted component size and the actual implantation results were compared to determine the accuracy. The templating time was compared to determine the efficiency. Furthermore, the potential factors influencing the accuracy of AI HIP were analyzed including sex, body mass index (BMI), and hip dysplasia.Results: The accuracy in predicting the acetabular cup and femoral stem was 74.58% and 71.19%, respectively, for AI HIP; 71.19% (P = 0.743) and 76.27% (P = 0.468), respectively, for 3D mimics; 40.68% (P < 0.001) and 49.15% (P = 0.021), respectively, for 2D digital templating. The templating time using AI HIP was 3.91±0.64 min, which was equivalent to 2D digital templates (2.96±0.48 min, P < 0.001), but shorter than 3D mimics (32.07±2.41 min, P < 0.001). Acetabular dysplasia(P = 0.021), rather than sex and BMI, was an influential factor in the accuracy of AI HIP templating. Compared to patients with developmental dysplasia of the hip (DDH), the accuracy of acetabular cup in the non-DDH group was better (P = 0.021), but the difference in the accuracy of the femoral stem between the two groups was statistically insignificant (P = 0.062).Conclusion: AI HIP showed excellent reliability for component size in THA. Acetabular dysplasia may affect the accuracy of AI HIP templating.

Analysis of smile aesthetics using the SmileCurves digital template

ABSTRACT Introduction: The aesthetic analysis of a smile may be facilitated by the use of a template that provides several dental aesthetic references and support for the diagnosis, simplifying it and defining guidelines for the aesthetic planning of orthodontic and integrated treatments. Objective: To describe a simple and objective procedure for the evaluation of smile aesthetics using the SmileCurves digital template (SCT), based on the superimposition of intraoral photographic images and close-up views of a smile. Conclusion: SCT is a simple and objective tool for the aesthetic analysis of a smile.