Application effect of computer-aided design combined with three-dimensional printing technology in autologous tooth transplantation: a retrospective cohort study

Combined application of artificial bone powders and concentrated growth factor membranes on the autotransplantation of mature third molars: A 5-year retrospective case series

AIM

This study aimed to observe the efficacy and outcomes of the combined application of artificial bone powder and concentrated growth factor (CGF) membranes for tooth transplantation in cases with wide recipient sockets and small donor teeth.

MATERIAL AND METHODS

A total of 36 teeth from 36 patients with wide recipient sockets and small donor teeth were enrolled. Autogenous tooth transplantation was performed using bone powders and CGF membranes. After transplantation, the visual analog scale (VAS) score, Landry Wound Healing Index, probing depth (PD), mobility, and gray value of the alveolar bone around the transplanted teeth were measured, and a patient satisfaction questionnaire was administered. All patients underwent clinical and radiographic examinations during follow-up.

RESULTS

The VAS score of 16 (44.4%) cases after 1 week was 0, and 26 (72.2%) patients showed excellent gingival healing after 2-4 weeks. The PD of a few cases was deeper than 3 mm during the first month; however, returned to normal after 3 months. Although the majority of the transplanted teeth possessed mobility greater than grade I during the first month, the mobility gradually improved within 3 months. The gray value of the alveolar bone around the transplanted teeth, 1 year postoperatively showed no difference with pre-operation (p > .05). During the mean follow-up period of 42.7 months (range 20-72 months), 33 of the 36 transplanted teeth remained in situ without clinical or radiographic complications, with an overall success rate of 91.7%.

CONCLUSIONS

Although the PD and mobility of the transplanted teeth were not ideal during the early stages of healing, most of the transplanted teeth had good clinical outcomes. In cases with large recipient sites accompanied by small donor teeth, autotransplantation of teeth using artificial bone powder combined with CGF membranes is a viable option and can lead to optimistic results with favorable success rates.

Accuracy assessment between computer-guided surgery planning and actual tooth position during tooth autotransplantation

BACKGROUND/AIM

This study aims to evaluate the precision and efficacy of utilizing computer-aided design (CAD) in combination with three-dimensional printing technology for tooth transplantation.

MATERIAL AND METHODS

This study analysed 50 transplanted teeth from 48 patients who underwent tooth transplantation surgery with the aid of CAD and positional guides. A consistent coordinate system was established using preoperative and postoperative cone-beam computed tomography images. Linear displacements and angular deviations were calculated by identifying key regions in both virtual designs and actual transplanted teeth. Additionally, an analysis was conducted to explore potential factors influencing these deviations.

RESULTS

The mean cervical deviation, apical deviation, and angular deviation among the 50 transplanted teeth were 1.16 ± 0.57 mm, 1.80 ± 0.94 mm, and 6.82 ± 3.14°, respectively. Cervical deviation was significantly smaller than apical deviation. No significant difference in deviation was observed among different recipient socket locations, holding true for both single-root, and multi-root teeth. However, a significant difference was noted in apical deviation between single-root and multi-root teeth. Our analysis identified a correlation between apical deviation and root length, leading to the development of a prediction model: Apical deviation = 0.1390 × (root length) + 0.2791.

CONCLUSIONS

The postoperative position of the donor teeth shows discrepancies compared to preoperative simulation when utilizing CAD and 3D printed templates during autotransplantation procedures. Continual refinement of preoperative design is a crucial endeavour.

Autologous Transplantation Tooth Guide Design Based on Deep Learning

BACKGROUND

Autologous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient cone-beam computed tomography (CBCT) scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated.

PURPOSE

We aimed to assess the feasibility of replacing the traditional design pipeline with a deep learning-enabled autologous tooth transplantation guide design pipeline.

STUDY DESIGN, SETTING, SAMPLE

This retrospective cross-sectional study used 79 CBCT images collected at the Guangzhou Medical University Hospital between October 2022 and March 2023. Following preprocessing, a total of 5,070 region of interest images were extracted from 79 CBCT images.

PREDICTOR VARIABLE

Autologous tooth transplantation guide design pipelines, either based on traditional manual design or deep learning-based design.

MAIN OUTCOME VARIABLE

The main outcome variable was the error between the reconstructed model and the gold standard benchmark. We used the third molar extracted clinically as the gold standard and leveraged it as the benchmark for evaluating our reconstructed models from different design pipelines. Both trueness and accuracy were used to evaluate this error. Trueness was assessed using the root mean square (RMS), and accuracy was measured using the standard deviation. The secondary outcome variable was the pipeline efficiency, assessed based on the time cost. Time cost refers to the amount of time required to acquire the third molar model using the pipeline.

ANALYSES

Data were analyzed using the Kruskal-Wallis test. Statistical significance was set at P < .05.

RESULTS

In the surface matching comparison for different reconstructed models, the deep learning group achieved the lowest RMS value (0.335 ± 0.066 mm). There were no significant differences in RMS values between manual design by a senior doctor and deep learning-based design (P = .688), and the standard deviation values did not differ among the 3 groups (P = .103). The deep learning-based design pipeline (0.017 ± 0.001 minutes) provided a faster assessment compared to the manual design pipeline by both senior (19.676 ± 2.386 minutes) and junior doctors (30.613 ± 6.571 minutes) (P < .001).

CONCLUSIONS AND RELEVANCE

The deep learning-based automatic pipeline exhibited similar performance in surgical guide design for autogenous tooth transplantation compared to manual design by senior doctors, and it minimized time costs.

Intentional replantation and dental autotransplantation of mandibular posterior teeth: Two case reports

BACKGROUND

Intentional replantation and dental autotransplantation are 2 similar techniques both involving atraumatic tooth extraction, visualization of the root, and replantation. They are considered as the last resort for unsalvageable teeth. The author aims to describe 2 mandibular posterior teeth with serious periapical lesions which are resolved by intentional replantation and dental autotransplantation, respectively.

CASE SUMMARY

In case 1, a 45-year-old male patient received root canal treatment because of a cracked mandible right first molar with periapical lesions. An endodontic file was separated in the apical third of the mesiolingual root canal. After conventional canal filling of the other root canals, the molar was atraumatically extracted. The separated instrument was removed, the mesiolingual root received a retrograde filling and the molar was replanted. At the 3-month follow up, the patient was asymptomatic and the X-ray picture showed no detectable root resorption and ankylosis. In case 2, a 29-year-old woman reported discomfort during occlusal loading after a root canal treatment and a coronal restoration of the mandibular right first molar. Radiographs showed a low-density shadow in the mesial apical and in the root furcation area of the mandibular first molar so the patient was diagnosed as chronic periapical periodontitis. After the removal of the affected tooth, the extraction socket was thoroughly debrided and irrigated. The intact mandibular right third molar with similar dimensions was extracted by minimally invasive procedure and transplanted. The donor tooth was fixed by a fiber-splint for 1 month and a root canal treatment was performed 2 weeks after surgery. After 1 year, clinical and radiographical examination revealed functional and periodontal healing.

CONCLUSIONS

These 2 reports present the successful management of intentional replantation and dental autotransplantation. Both procedures are recommended after nonsurgical endodontic treatment, especially when apical microsurgery is not an option, for example because of difficult accessibility or patient preference.

Combined Application of Virtual Simulation Technology and 3-Dimensional-Printed Computer-Aided Rapid Prototyping in Autotransplantation of a Mature Third Molar

The use of computer-aided rapid prototyping (CARP) models was considered to reduce surgical trauma and improve outcomes when autotransplantation of teeth (ATT) became a viable alternative for dental rehabilitation. However, ATT is considered technique-sensitive due to its series of complicated surgical procedures and unfavorable outcomes in complex cases. This study reported a novel autotransplantation technique of a 28-year-old patient with an unrestorable lower first molar (#36) with double roots. Regardless of a large shape deviation, a lower third molar (#38) with a completely single root formation was used as the donor tooth. ATT was performed with a combined use of virtual simulation, CARP model-based rehearsed surgery, and tooth replica-guided surgery. A 3D virtual model of the donor and recipient site was generated from cone-beam computed tomographic (CBCT) radiographs prior to surgery for direct virtual superimposition simulation and CARP model fabrication. The virtual simulation indicated that it was necessary to retain cervical alveolar bone during the surgical socket preparation, and an intensive surgical rehearsal was performed on the CARP models. The donor tooth replica was used during the procedure to guide precise socket preparation and avoid periodontal ligament injury. Without an additional fitting trial and extra-alveolar storage, the donor tooth settled naturally into the recipient socket within 30 s. The transplanted tooth showed excellent stability and received routine root canal treatment three weeks post-surgery, and the one-year follow-up examination verified the PDL healing outcome and normal functioning. Patient was satisfied with the transplanted tooth. This cutting-edge technology combines virtual simulation, digital surgery planning, and guided surgery implementation to ensure predictable and minimally invasive therapy in complex cases.

The Computer-Aided Design Algorithm of Dyeing and Weaving Graphics from the Perspective of Public Art

With the acceleration of economic development, people put forward higher requirements for clothing style. In this context, the application of traditional patterns has good artistic effects, can show a unique style, and can also express the artistic beauty of clothing through deformation, color distribution, and other forms, and occupies a relatively large position in the design process of dyeing and weaving art. Aiming at the problem of inaccurate extraction of image information from known visual scenes in the original art-aided design, resulting in unclear output images, this paper proposes a computer-aided design algorithm for dyeing and weaving graphics in the field of public art by color segmentation of the known visual scene images according to the set threshold, morphological processing of the segmented images, reducing noise and fractures affecting the acquired connected areas, and formulating extraction rules to screen candidate areas. Furthermore, dense sampling form is used to extract more scale invariant feature transform (SIFT) target features in the candidate area, match the feature points, integrate the coordinate system of known image information into a unified coordinate system, output the design image, and complete the auxiliary design of dyeing and weaving graphics. The results of simulation experiments show that the computer-aided design algorithm of dyeing and weaving graphics in the public art field is more accurate than the original method in extracting information from known images, which helps to solve the problem of clear output dyeing and weaving images, and improves the quality of dyeing and weaving images.