During orthodontic treatment, clinical monitoring of patients is a crucial factor in determining treatment success. It aids in timely problem detection and resolution, ensuring adherence to the intended treatment plan. In recent years, digital technology has increasingly permeated orthodontic clinical diagnosis and treatment, facilitating clinical decision-making, treatment planning, and follow-up monitoring. This review summarizes recent advancements in digital technology for monitoring orthodontic tooth movement, related complications, and appliance-wearing compliance. It aims to provide insights for researchers and clinicians to enhance the application of digital technology in orthodontics, improve treatment outcomes, and optimize patient experience. The digitization of diagnostic data and the visualization of dental models make chair-side follow-up monitoring more convenient, accurate, and efficient. At the same time, the emergence of remote monitoring technology allows orthodontists to promptly identify oral health issues in patients and take corresponding measures. Furthermore, the multimodal data fusion method offers valuable insights into the monitoring of the root-alveolar relationship. Artificial intelligence technology has made initial strides in automating the identification of orthodontic tooth movement, associated complications, and patient compliance evaluation. Sensors are effective tools for monitoring patient adherence and providing data-driven support for clinical decision-making. The application of digital technology in orthodontic monitoring holds great promise. However, challenges like technical bottlenecks, ethical considerations, and patient acceptance remain.
Timm LH, Farrag G, Baxmann M, et al. Factors influencing patient compliance during clear aligner therapy: a retrospective cohort study [J]. J Clin Med, 2021, 10(14): 3103. doi: 10.3390/jcm10143103.
Gao J, Xu YR, Zhang HL, et al. Clinical supervision during the clear aligner treatment [J]. Chin J Orthod, 2022, 29(1): 42-46. doi: 10.3760/cma.j.cn115797-20210908-22109.
Dipalma G, Inchingolo AD, Inchingolo AM, et al. Artificial intelligence and its clinical applications in orthodontics: a systematic review [J]. Diagnostics(Basel), 2023, 13(24): 3677. doi: 10.3390/diagnostics13243677.
Impellizzeri A, Horodynski M, De Stefano A, et al. CBCT and intra-oral scanner: the advantages of 3D technologies in orthodontic treatment [J]. Int J Environ Res Public Health, 2020, 17(24): 9428. doi: 10.3390/ijerph17249428.
Logan S, Riedy CA, Hargett K, et al. Orthodontists' use of remote monitoring platforms pre-, amid, and post-COVID-19: a survey study [J]. BMC Oral Health, 2024, 24(1): 480. doi: 10.1186/s12903-024-04245-2.
Lam J, Freer E, Miles P. Comparative assessment of treatment efficiency and patient experience between dental monitoring and conventional monitoring of clear aligner therapy: a single-center randomized controlled trial [J]. Am J Orthod Dentofacial Orthop, 2023, 163(4): 456-464. doi: 10.1016/j.ajodo.2022.12.004.
Hansa I, Katyal V, Ferguson DJ, et al. Outcomes of clear aligner treatment with and without dental monitoring: a retrospective cohort study [J]. Am J Orthod Dentofacial Orthop, 2021, 159(4): 453-459. doi: 10.1016/j.ajodo.2020.02.010.
Nordblom NF, Büttner M, Schwendicke F. Artificial intelligence in orthodontics: critical review [J]. J Dent Res, 2024, 103(6): 577-584. doi: 10.1177/00220345241235606.
Sangalli L, Savoldi F, Dalessandri D, et al. Remote digital monitoring during the retention phase of orthodontic treatment: a prospective feasibility study [J]. Korean J Orthod, 2022, 52(2): 123-130. doi: 10.4041/kjod.2022.52.2.123.
Ubuzima P, Nshimiyimana E, Mukeshimana C, et al. Exploring biological mechanisms in orthodontic tooth movement: bridging the gap between basic research experiments and clinical applications-a comprehensive review [J]. Ann Anat, 2024, 255: 152286. doi: 10.1016/j.aanat.2024.152286.
Jia YF, Bao XF, Hu M. Dental monitoring in clear aligner treatment [J]. Chin J Stomatol, 2024, 59(3): 295-300. doi: 10.3760/cma.j.cn112144-20240103-00003.
Palone M, Bellavia M, Floris M, et al. Evaluation of effects of brackets and orthodontic wires on intraoral scans: a prospective in-vivo study [J]. Orthod Craniofac Res, 2024, 27(1): 44-54. doi: 10.1111/ocr.12682.
Jánosi KM, Cerghizan D, Mártha KI, et al. Evaluation of intraoral full-arch scan versus conventional preliminary impression [J]. J Clin Med, 2023, 12(17): 5508. doi: 10.3390/jcm12175508.
Amornvit P, Rokaya D, Sanohkan S. Comparison of accuracy of current ten intraoral scanners [J]. Biomed Res Int, 2021, 2021: 2673040. doi: 10.1155/2021/2673040.
Rutkūnas V, Jegelevičius D, Gedrimienė A, et al. Effect of different intraoral scanners on the accuracy of bite registration in edentulous maxillary and mandibular arches [J]. J Dent, 2024, 146: 105050. doi: 10.1016/j.jdent.2024.105050.
Revilla-León M, Ntovas P, Kois JC. Locating centric occlusion by using intraoral scanners and open access or dental computer-aided design programs: a dental technique [J]. J Prosthet Dent, 2024. doi: 10.1016/j.prosdent.2024.05.002.
Kang SJ, Kee YJ, Lee KC. Effect of the presence of orthodontic brackets on intraoral scans [J]. Angle Orthod, 2021, 91(1): 98-104. doi: 10.2319/040420-254.1.
Tian Y, Jian G, Wang J, et al. A revised approach to orthodontic treatment monitoring from oralscan video [J]. IEEE J Biomed Health Inform, 2023, 27(12): 5827-5836. doi: 10.1109/JBHI.2023.3319361.
Morris RS, Hoye LN, Elnagar MH, et al. Accuracy of dental monitoring 3D digital dental models using photograph and video mode [J]. Am J Orthod Dentofacial Orthop, 2019, 156(3): 420-428. doi: 10.1016/j.ajodo.2019.02.014.
Arqub SA, Nedjat-Haiem M, Einbinder M, et al. Characterizing orthodontic tooth movement in real time using dental monitoring scans: a pilot study [J]. Orthod Craniofac Res, 2023, 26(Suppl 1): 82-91. doi: 10.1111/ocr.12717.
Homsi K, Snider V, Kusnoto B, et al. In-vivo evaluation of artificial intelligence driven remote monitoring technology for tracking tooth movement and reconstruction of 3-dimensional digital models during orthodontic treatment [J]. Am J Orthod Dentofacial Orthop, 2023, 164(5): 690-699. doi: 10.1016/j.ajodo.2023.04.019.
Homsi K, Ramachandran V, Del Campo DM, et al. The use of teleorthodontics during the COVID-19 pandemic and beyond-perspectives of patients and providers [J]. BMC Oral Health, 2023, 23(1): 490. doi: 10.1186/s12903-023-03215-4.
Li B, Xu YM, Shi RY, et al. Accuracy of progress assessment with clear aligners [J]. West China J Stomatol, 2022, 40(6): 698-703. doi: 10.7518/hxkq.2022.06.011.
Hansa I, Semaan SJ, Vaid NR. Clinical outcomes and patient perspectives of Dental Monitoring® GoLive® with Invisalign®-a retrospective cohort study [J]. Prog Orthod, 2020, 21(1): 16. doi: 10.1186/s40510-020-00316-6.
Ferlito T, Hsiou D, Hargett K, et al. Assessment of artificial intelligence-based remote monitoring of clear aligner therapy: a prospective study [J]. Am J Orthod Dentofacial Orthop, 2023, 164(2): 194-200. doi: 10.1016/j.ajodo.2022.11.020.
Zhang X, Gao J, Sun W, et al. Evaluation of alveolar bone morphology of incisors with different sagittal skeletal facial types by cone beam computed tomography: a retrospective study [J]. Heliyon, 2023, 9(4): e15369. doi: 10.1016/j.heliyon.2023.e15369.
Pi S, Geng J, Bajestan M, et al. Accuracy and reliability of the expected root position setup to evaluate root proximity of an edentulous site [J]. Am J Orthod Dentofacial Orthop, 2022, 162(5): 753-762. doi: 10.1016/j.ajodo.2022.05.019.
Lim SW, Park H, Lim SY, et al. Can we estimate root axis using a 3-dimensional tooth model via lingual-surface intraoral scanning? [J]. Am J Orthod Dentofacial Orthop, 2020, 158(5): e99-e109. doi: 10.1016/j.ajodo.2020.07.032.
Lim SW, Moon RJ, Kim MS, et al. Construction reproducibility of a composite tooth model composed of an intraoral-scanned crown and a cone-beam computed tomography-scanned root [J]. Korean J Orthod, 2020, 50(4): 229-237. doi: 10.4041/kjod.2020.50.4.229.
Tüfekçi E, Carrico CK, Gordon CB, et al. Does crown, root, and bone visualization in a clear aligner virtual setup impact treatment decisions? [J]. Am J Orthod Dentofacial Orthop, 2024, 165(6): 671-679. doi: 10.1016/j.ajodo.2024.01.014.
Hu X, Zhao Y, Yang C. Evaluation of root position during orthodontic treatment via multiple intraoral scans with automated registration technology [J]. Am J Orthod Dentofacial Orthop, 2023, 164(2): 285-292. doi: 10.1016/j.ajodo.2023.04.012.
Marincak Vrankova Z, Rousi M, Cvanova M, et al. Effect of fixed orthodontic appliances on gingival status and oral microbiota: a pilot study [J]. BMC Oral Health, 2022, 22(1): 455. doi: 10.1186/s12903-022-02511-9.
Shokeen B, Viloria E, Duong E, et al. The impact of fixed orthodontic appliances and clear aligners on the oral microbiome and the association with clinical parameters: a longitudinal comparative study [J]. Am J Orthod Dentofacial Orthop, 2022, 161(5): e475-e485. doi: 10.1016/j.ajodo.2021.10.015.
Nichols GAL, Broadbent JM, Olliver S, et al. Are changes in malocclusion associated with adulthood psychosocial well-being? [J]. Am J Orthod Dentofacial Orthop, 2024. doi: 10.1016/j.ajodo.2024.04.013.
Zheng J, Wang X, Zhang T, et al. Comparative characterization of supragingival plaque microbiomes in malocclusion adult female patients undergoing orthodontic treatment with removable aligners or fixed appliances: a descriptive cross-sectional study [J]. Front Cell Infect Microbiol, 2024, 14: 1350181. doi: 10.3389/fcimb.2024.1350181.
Zibar Belasic T, Badnjevic M, Zigante M, et al. Supragingival dental biofilm profile and biofilm control during orthodontic treatment with fixed orthodontic appliance: a randomized controlled trial [J]. Arch Oral Biol, 2024, 164: 105984. doi: 10.1016/j.archoralbio.2024.105984.
Klukowska M, Bader A, Erbe C, et al. Plaque levels of patients with fixed orthodontic appliances measured by digital plaque image analysis [J]. Am J Orthod Dentofacial Orthop, 2011, 139(5): e463-e470. doi: 10.1016/j.ajodo.2010.05.019.
Ozsunkar PS, Özen DÇ, Abdelkarim AZ, et al. Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study [J]. BMC Oral Health, 2024, 24(1): 490. doi: 10.1186/s12903-024-04262-1.
Erbe C, Hartmann L, Schmidtmann I, et al. A novel method quantifying caries following orthodontic treatment [J]. Sci Rep, 2021, 11(1): 21347. doi: 10.1038/s41598-021-00561-7.
Miller CC, Burnside G, Higham SM, et al. Quantitative light-induced fluorescence-digital as an oral hygiene evaluation tool to assess plaque accumulation and enamel demineralization in orthodontics [J]. Angle Orthod, 2016, 86(6): 991-997. doi: 10.2319/092415-648.1.
Alasiri MM, Almalki A, Alotaibi S, et al. Association between gingival phenotype and periodontal disease severity-a comparative longitudinal study among patients undergoing fixed orthodontic therapy and invisalign treatment [J]. Healthcare(Basel), 2024, 12(6): 656. doi: 10.3390/healthcare12060656.
Kim HN, Kim K, Lee Y. Intra-oral photograph analysis for gingivitis screening in orthodontic patients [J]. Int J Environ Res Public Health, 2023, 20(4): 3705. doi: 10.3390/ijerph20043705.
Daly S, Seong J, Parkinson C, et al. A proof of concept study to confirm the suitability of an intra oral scanner to record oral images for the non-invasive assessment of gingival inflammation [J]. J Dent, 2021, 105: 103579. doi: 10.1016/j.jdent.2020.103579.
Alalharith DM, Alharthi HM, Alghamdi WM, et al. A deep learning-based approach for the detection of early signs of gingivitis in orthodontic patients using faster region-based convolutional neural networks [J]. Int J Environ Res Public Health, 2020, 17(22): 8447. doi: 10.3390/ijerph17228447.
Lo Russo L, Zhurakivska K, Montaruli G, et al. Effects of crown movement on periodontal biotype: a digital analysis [J]. Odontology, 2018, 106(4): 414-421. doi: 10.1007/s10266-018-0370-5.
Caruso S, Caruso S, Pellegrino M, et al. A knowledge-based algorithm for automatic monitoring of orthodontic treatment: the dental monitoring system. Two cases [J]. Sensors(Basel), 2021, 21(5): 1856. doi: 10.3390/s21051856.
Snider V, Homsi K, Kusnoto B, et al. Effectiveness of AI-driven remote monitoring technology in improving oral hygiene during orthodontic treatment [J]. Orthod Craniofac Res, 2023, 26(Suppl 1): 102-110. doi: 10.1111/ocr.12666.
Sangalli L, Savoldi F, Dalessandri D, et al. Effects of remote digital monitoring on oral hygiene of orthodontic patients: a prospective study [J]. BMC Oral Health, 2021, 21(1): 435. doi: 10.1186/s12903-021-01793-9.
Snider V, Homsi K, Kusnoto B, et al. Clinical evaluation of artificial intelligence driven remote monitoring technology for assessment of patient oral hygiene during orthodontic treatment [J]. Am J Orthod Dentofacial Orthop, 2024, 165(5): 586-592. doi: 10.1016/j.ajodo.2023.12.008.
Tobias G, Spanier AB. Developing a mobile app(iGAM)to promote gingival health by professional monitoring of dental selfies: user-centered design approach [J]. JMIR Mhealth Uhealth, 2020, 8(8): e19433. doi: 10.2196/19433.
Nahajowski M, Lis J, Sarul M. Orthodontic compliance assessment: a systematic review [J]. Int Dent J, 2022, 72(5): 597-606. doi: 10.1016/j.identj.2022.07.004.
Wafaie K, Rizk MZ, Basyouni ME, et al. Tele-orthodontics and sensor-based technologies: a systematic review of interventions that monitor and improve compliance of orthodontic patients [J]. Eur J Orthod, 2023, 45(4): 450-461. doi: 10.1093/ejo/cjad004.
Thurzo A, Kurilová V, Varga I. Artificial intelligence in orthodontic smart application for treatment coaching and its impact on clinical performance of patients monitored with AI-teleHealth system [J]. Healthcare(Basel), 2021, 9(12): 1695. doi: 10.3390/healthcare9121695.
Kutay C, Kılıçoğlu H, Sayar G. Comparison of objective wear time between monoblock and twin-block appliances measured by microsensor [J]. Angle Orthod, 2021, 91(6): 749-755. doi: 10.2319/021421-128.1.
Arponen H, Hirvensalo R, Lindgren V, et al. Treatment compliance of adolescent orthodontic patients with headgear activator and twin-block appliance assessed prospectively using microelectronic wear-time documentation [J]. Eur J Orthod, 2020, 42(2): 180-186. doi: 10.1093/ejo/cjaa001.
He R, Liu H, Niu Y, et al. Flexible miniaturized sensor technologies for long-term physiological monitoring [J]. NPJ Flex Electron, 2022, 6: 20. doi: 10.1038/s41528-022-00146-y.
Nahajowski M, Lis J, Sarul M. The use of microsensors to assess the daily wear time of removable orthodontic appliances: a prospective cohort study [J]. Sensors(Basel), 2022, 22(7): 2435. doi: 10.3390/s22072435.
Sarul M, Nahajowski M, Gawin G, et al. Does daily wear time of twin block reliably predict its efficiency of class Ⅱ treatment? [J]. J Orofac Orthop, 2022, 83(3): 195-204. doi: 10.1007/s00056-021-00300-7.
Frilund E, Sonesson M, Magnusson A. Patient compliance with twin block appliance during treatment of Class Ⅱ malocclusion: a randomized controlled trial on two check-up prescriptions [J]. Eur J Orthod, 2023, 45(2): 142-149. doi: 10.1093/ejo/cjac046.
Ioerger P, Afshari A, Hentati F, et al. Mandibular advancement vs combined airway and positional therapy for snoring: a randomized clinical trial [J]. JAMA Otolaryngol Head Neck Surg, 2024, 23: e241035. doi: 10.1001/jamaoto.2024.1035.
Jafarimehrabady N, Scribante A, Defabianis P, et al. A systematic review of oral modifications caused by the prolonged application of continuous positive airway pressure (CPAP) and intraoral appliances in patients with obstructive sleep apnea (OSA) [J]. Biomed Res Int, 2024, 2024: 9361528. doi: 10.1155/2024/9361528.
Kwon JS, Jung HJ, Yu JH, et al. Effectiveness of remote monitoring and feedback on objective compliance with a mandibular advancement device for treatment of obstructive sleep apnea [J]. J Sleep Res, 2022, 31(3): e13508. doi: 10.1111/jsr.13508.