ABSTRACT
Background:
The efficacy of clear aligner therapy (CAT) in the management of malocclusion is largely dependent on patient compliance, especially the proper use of aligners. Objective: The present study investigated the influence of an artificial intelligence (AI) mobile application (StrojCHECK®) on patient participation and follow-through with CAT treatment regimens.
Materials and Methods:
Fifty-five patients (33 women, 22 men) aged 12-68 years were tracked pre- and post-an update to the app’s AI system with features such as decision tree algorithms, individualized feedback, and real-time alerting for patients and clinicians.
Results:
Our findings indicated increased patient app engagement scores from 4.2 to 7.3 and improved clinical aligner tracking adherence from 85% to 92%. Gender-based analysis showed a highly significant decrease in non-compliance among women participants (from 10% to 5%, P < 0.05), whereas the compliance of males did not change.
Conclusion:
The investigation emphasizes the crucial role of collecting behavioral data and individualized feedback in improving the outcome of treatments. The adoption of AI technology in digital healthcare solutions has vast potential to increase patient compliance and maximize orthodontic treatment.
KEYWORDS: Artificial intelligence, clear aligner therapy, orthodontic treatment, smart application
INTRODUCTION
Malocclusion and orthodontic treatment needs are prevalent, with a considerable percentage of the population having visible incisor irregularity and bite abnormalities, with 57%-59% needing some treatment.[1] Although both braces and clear aligners are effective, clear aligners have the benefit of segmented tooth movement and reduced treatment times but are inferior to braces in guaranteeing occlusal contacts, torque control, and retention. Clear aligner therapy (CAT) is very good at arch alignment and regulation of tooth movements but is less effective in anterior extrusion, anterior buccolingual inclination, and tipping rounded teeth.[2,3] The CAT branch is developing due to technological developments, such as optimized attachment systems and artificial intelligence (AI) mobile apps for improving treatment outcomes. This study was conducted to assess the support of CAT with smart mobile applications and AI.
MATERIALS AND METHODS
Fifty-five participants (33 females, 22 males) between the ages of 12 and 68 years were recruited and monitored for orthodontic treatment coaching using the StrojCHECK® mobile app. The app was used by all participants 60 days before and following an update in the app’s AI system to assess the behavior of patients and app usage. The research lacked a control group, and the technology (dental monitoring) employed is clinically proven and validated, so no special approval for human trials was needed. The StrojCHECK® app recorded different patient-reported data, such as eating, drinking, brushing, and aligner wear behavior, continuously recorded through the mobile app and, in certain instances, through wearable devices such as Apple Watch®. The application used phone sensors to monitor habits including frequency of cleaning of teeth, use of aligner, and frequency of eating and drinking. It also provided motivational incentives such as badges for treatment routine compliance, and its back end calculated this information to send reminders or rewards to enhance patient compliance.
The AI-powered update had some important features like system activation, whereby alerts are raised when patients’ performance falls short of expectations, doctor activation, whereby low-performing users are highlighted and clinicians are alerted, and patient activation, whereby personal weekly reports to patients are delivered to promote reflection and improved adherence. Fifty-five participants’ data were analyzed through descriptive statistics, Wilcoxon signed-rank tests, and Spearman’s correlation coefficient to assess the effect of the update on patient performance. Statistical analysis was conducted with Microsoft Excel 2016, Statistica 13.1, and StatsDirect 3.3.5, and a P value of <0.05 was used as statistically significant. The back end of the app consisted of a cloud-based server with Ubuntu/Linux, a MySQL 8 database, and PHP 8 programming language, whereas the mobile front-end was created using Vue.js and Ionic 5 to support hybrid development for both iOS and Android platforms.
RESULTS
The results of the study indicated that the app update incorporating decision tree algorithms significantly improved patient engagement and aligner compliance. After the update, patient app interaction scores increased from 4.2 to 7.3 [Figure 1]. Clinical aligner tracking also showed a significant improvement, with adherence rising from 85% to 92% [Figure 2]. However, the manifestation of clinical non-tracking remained unchanged for men (P = 0.45), while women showed a significant reduction in non-tracking [from 10% to 5%, P < 0.05; Table 1].
Figure 1.
Impact of app update on patient app interaction
Figure 2.
Impact of app update on clinical aligner tracking
Table 1.
Non-tracking manifestation by gender
| Gender | Before app update (%) | After app update (%) | P value |
|---|---|---|---|
| Resin versus ZP | 1138.51 | 0.015 | |
| Resin versus GIC | 925.65 | 0.015 | |
| GIC versus ZP | 212.86 | 0.015 |
DISCUSSION
The results of our study emphasize the importance of patient behavior and motivation in CAT success. Our data showed that although CAT is successful in correcting malocclusion, patient compliance with treatment regimens, especially the regular use of aligners, significantly affects its success. Forgetfulness on the part of the patient, often cited as a key impediment to effective treatment, was recognized as a major impediment to optimal clinical results. This is consistent with an earlier study that has indicated patient non-compliance can critically undermine the efficacy of orthodontic treatments, particularly when aligners are not worn for more than 120 minutes a day.[4]
Our approach, which integrates AI-enabled features in a smart healthcare app, presents potential solutions to meet this challenge. With the combination of behavioral data collection and personalized feedback, the app can give real-time insights into a patient’s treatment progress and adherence, enabling a more personalized method of orthodontic treatment. This customized system allows healthcare professionals to tailor motivational reactions and treatment strategies based on individual patient requirements, enhancing the potential for long-term participation and enhanced clinical outcomes. In addition, our results validate the concept that having a smart app monitor and correct the patient’s behavior can not only encourage improved compliance but also enable the practitioner to intervene early when non-compliance is identified, thereby mitigating the risk of adverse treatment outcomes.
Moreover, this research highlights the importance of gaining insights into patient-specific behavioral patterns. Existing studies of telehealth platforms and AI in other areas of medicine have indicated that individualized data-driven interventions are more likely to improve patient outcomes.[5,6,7,8] Our research is based on this principle by suggesting that AI programs not only observe compliance but also modify motivational tactics according to individual patients’ distinctive behavioral patterns. This method accords with the idea of computer-based learning, which is based on interactive data-driven systems that can effectively involve users and adjust to their learning or treatment requirements.[9]
Notably, our findings also indicated that although AI enhancements and intelligent app capabilities have the potential to greatly increase patient compliance and engagement, they need to be phased in slowly, beginning with basic computerized learning capabilities. Stepwise implementation will enable both patients and physicians to adapt to the system, such that the more advanced AI algorithms can be efficiently used for maximum benefit.[9]
The wider significance of our research is the urgent need for dentistry to explore and understand digital health technologies. As the profession continues to evolve, dental practices need to adopt digital workflows and tools to enhance treatment outcomes, facilitate patient management, and prepare for emerging trends in healthcare delivery. The incorporation of AI and smart healthcare apps is a critical step toward the modernization of dental practice and enhanced patient-centered care.
In conclusion, our findings demonstrated that the integration of AI-driven apps with customized treatment plans holds great promise for improving CAT compliance and overall treatment success. The long-term effects of these digital interventions on patient outcomes and how they can be incorporated into clinical practice need to be investigated in future studies.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
REFERENCES
- 1.Proffit WR, Fields H, Jr, Moray L. Prevalence of malocclusion and orthodontic treatment need in the United States: Estimates from the NHANES III survey. Int J Adult Orthodon Orthognath Surg. 1998;13:97–106. [PubMed] [Google Scholar]
- 2.Ke Y, Zhu Y, Zhu M. A comparison of treatment effectiveness between clear aligner and fixed appliance therapies. BMC Oral Health. 2019;19:1–10. doi: 10.1186/s12903-018-0695-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Rossini G, Parrini S, Castroflorio T, Deregibus A, Debernardi CL. Efficacy of clear aligners in controlling orthodontic tooth movement: A systematic review. Angle Orthod. 2015;85:881–9. doi: 10.2319/061614-436.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Bowman SJ, editor. Seminars in Orthodontics. WB Saunders, Elsevier; 2017. Improving the predictability of clear aligners; pp. 65–75. [Google Scholar]
- 5.Mucchi L, Jayousi S, Gant A, Paoletti E, Zoppi P. Tele-monitoring system for chronic diseases management: Requirements and architecture. Int J Environ Res Public Health. 2021;18:7459. doi: 10.3390/ijerph18147459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Muehlensiepen F, Kurkowski S, Krusche M, Mucke J, Prill R, Heinze M, et al. Digital health transition in rheumatology: A qualitative study. Int J Environ Res Public Health. 2021;18:2636. doi: 10.3390/ijerph18052636. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Donner CF, ZuWallack R, Nici L. The role of telemedicine in extending and enhancing medical management of the patient with chronic obstructive pulmonary disease. Medicina. 2021;57:726. doi: 10.3390/medicina57070726. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Corea F, Ciotti S, Cometa A, De Carlo C, Martini G, Baratta S, et al. Telemedicine during the coronavirus disease (COVID-19) pandemic: A multiple sclerosis (MS) outpatients service perspective. Neurol Int. 2021;13:25–31. doi: 10.3390/neurolint13010003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.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. Healthcare (Basel) 2021;9:1695. doi: 10.3390/healthcare9121695. [DOI] [PMC free article] [PubMed] [Google Scholar]
