The last decade (2010‐2021) has witnessed the evolution of robotic applications in orthodontics. This review scopes and analyzes published orthodontic literature in eight different domains: (1) robotic dental assistants; (2) robotics in diagnosis and simulation of orthodontic problems; (3) robotics in orthodontic patient education, teaching, and training; (4) wire bending and customized appliance robotics; (5) nanorobots/microrobots for acceleration of tooth movement and for remote monitoring; (6) robotics in maxillofacial surgeries and implant placement; (7) automated aligner production robotics; and (8) TMD rehabilitative robotics. A total of 1,150 records were searched, of which 124 potentially relevant articles were retrieved in full. 87 studies met the selection criteria following screening and were included in the scoping review. The review found that studies pertaining to arch wire bending and customized appliance robots, simulative robots for diagnosis, and surgical robots have been important areas of research in the last decade (32%, 22%, and 16%). Rehabilitative robots and nanorobots are quite promising and have been considerably reported in the orthodontic literature (13%, 9%). On the other hand, assistive robots, automated aligner production robots, and patient robots need more scientific data to be gathered in the future (1%, 1%, and 6%). Technological readiness of different robotic applications in orthodontics was further assessed. The presented eight domains of robotic technologies were assigned to an estimated technological readiness level according to the information given in the publications. Wire bending robots, TMD robots, nanorobots, and aligner production robots have reached the highest levels of technological readiness: 9; diagnostic robots and patient robots reached level 7, whereas surgical robots and assistive robots reached lower levels of readiness: 4 and 3, respectively.

Background: To evaluate the accuracy of three different 3D digital model registration software packages for linear tooth movement measurements, with reference to a 3D digital virtual setup (DS). Methods: Twenty maxillary and mandibular pre-treatment scans of patients undergoing clear aligner therapy were used. Digital Setups were generated from pre-treatment scans using OrthoAnalyzer software. Both the pretreatment digital scans (T1) and Digital Setups (T2) were converted to STL files to be imported to the three studied software packages: Geomagic, OrthoAnalyzer and Compare. Linear changes in tooth positions were calculated for all the registered pairs. Results: The change in tooth position was compared between the calculated tooth movement using each of the registration software packages versus the actual generated tooth movement from the Digital Setups. Continuous data was expressed as mean and standard deviation. Intraclass Correlation Coefficients for agreements between Digital Simulation and each software was used. Intra and Inter-examiner reliabilities were also assessed using Intraclass Correlation Coefficients. Significance of the obtained results was expressed at p ≤ 0.01. Geomagic software showed agreements > 0.90 for maxillary linear tooth movements and between 0.75 and 0.90 for mandibular measurements. OrthoAnalyzer software showed agreements between 0.50 and < 0.75 for maxillary and mandibular measurements. Compare software showed agreements > 0.90 for maxillary and mandibular linear tooth movements, indicating the best consistency. Conclusions: Compare and Geomagic software packages consistently showed maximum accuracy in measuring the amount of tooth movement in the maxillary arch compared to the reference standard. Compare software showed the highest agreements in the mandibular arch. None of the three studied software packages showed poor agreement with the Digital Setup across all tooth movement measurements. Buccolingual tooth movements showed the highest agreements amongst linear measurements.

Background: To investigate the accuracy of three different 3D digital model registration software for tip, torque and rotation measurements, with reference to a 3D digital virtual setup. Twenty maxillary and mandibular pre-treatment scans of patients undergoing clear aligner therapy were used. Digital setups were generated from pre-treatment scans using a tooth movement software. Both the pretreatment digital scans (T1) and digital setups (T2) were converted to STL files to be exported to the 3 studied software that employed: (1) Semiautomatic best fit registration (S-BF), (2) Interactive surface-based registration (I-SB), and (3) Automatic best fit registration (A-BF) respectively. Changes in tip, torque and rotation were calculated for all the registered pairs. Results: The change in tooth position was compared between the calculated tooth movement using each of the registration software packages versus the actual generated tooth movement from the digital setups. Continuous data was expressed as mean and standard deviation. Intra Class Correlation Coefficient for agreement between digital simulation and each software was used. Intra and Inter-examiner reliabilities were also assessed using Intra Class Correlation Coefficient. Significance of the obtained results was expressed at p ≤ 0.01. Semiautomatic best fit registration software showed excellent agreement (> 0.90) for all tooth movements, except for good agreement for torque (0.808). Interactive surface-based registration software showed moderate agreement for all measurements (0.50 and < 0.75), except for good agreement for rotation (0.783). Automatic best fit registration software demonstrated excellent agreement (> 0.90) for rotation, good agreement for tip (0.890) and moderate agreement for torque (0.740). Conclusions: Overall, semiautomatic best fit registration software consistently showed excellent agreement in superimpositions compared to other software types. Automatic best fit registration software consistently demonstrated better agreement for mandibular superimpositions, compared to others. Accuracy of digital model superimpositions for tooth movements studied in superimposition studies, can be attributed to the algorithm employed for quantification.