Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System

Treatment of malocclusion with clear removable appliances like Invisalign or Spark™, require considerable higher level of patient compliance when compared to conventional fixed braces. The clinical outcomes and treatment efficiency strongly depend on the patient's discipline. Smart treatment co...

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Bibliographic Details
Published in:Healthcare (Basel) 2021-12, Vol.9 (12), p.1695
Main Authors: Thurzo, Andrej, Kurilová, Veronika, Varga, Ivan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
behavior change techniques
clear aligners
computerized learning
Dental occlusion
Oral hygiene
orthodontic treatment
Orthodontics
Patients
smart application
Smartphones
Teeth
Telemedicine
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Summary:Treatment of malocclusion with clear removable appliances like Invisalign or Spark™, require considerable higher level of patient compliance when compared to conventional fixed braces. The clinical outcomes and treatment efficiency strongly depend on the patient's discipline. Smart treatment coaching applications, like strojCHECK are efficient for improving patient compliance. To evaluate the impact of computerized personalized decision algorithms responding to observed and anticipated patient behavior implemented as an update of an existing clinical orthodontic application (app). Variables such as (1) patient app interaction, (2) patient app discipline and (3) clinical aligner tracking evaluated by artificial intelligence system (AI) system-Dental monitoring were observed on the set of 86 patients. Two 60-day periods were evaluated; before and after the app was updated with decision tree processes. All variables showed significant improvement after the update except for the manifestation of clinical non-tracking in men, evaluated by artificial intelligence from video scans. Implementation of application update including computerized decision processes can significantly enhance clinical performance of existing health care applications and improve patients' compliance. Using the algorithm with decision tree architecture could create a baseline for further machine learning optimization.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare9121695