This study aimed to test the accuracy of the 3-dimensional (3D) digital dental models generated by the Dental Monitoring (DM) smartphone application in both photograph and video modes over successive DM examinations in comparison with 3D digital dental models generated by the iTero Element intraoral scanner. Methods: Ten typodonts with setups of class I malocclusion and comparable severity of anterior crowding were used in the study. iTero Element scans along with DM examination in photograph and video modes were performed before tooth movement and after each set of 10 Invisalign aligners for each typodont. Stereolithography (STL) files generated from the DM examinations in photograph and video modes were superimposed with the STL files from the iTero scans using GOM Inspect software to determine the accuracy of both photograph and video modes of DM technology. Results: No clinically significant differences, according to the American Board of Orthodontics-determined standards, were found. Mean global deviations for the maxillary arch ranged from 0.00149 to 0.02756 mm in photograph mode and from 0.0148 to 0.0256 mm in video mode. Mean global deviations for the mandibular arch ranged from 0.0164 to 0.0275 mm in photograph mode and from 0.0150 to 0.0264 mm in video mode. Statistically significant differences were found between the 3D models generated by the iTero and the DM application in photograph and video modes over successive DM examinations. Conclusions: 3D digital dental models generated by the DM smartphone application in photograph and video modes are accurate enough to be used for clinical applications.

Structured Abstract The objective of this report was to provide an overview of the current landscape of big data analytics in the healthcare sector, introduce various approaches of machine learning and discuss potential implications in the field of orthodontics. With the increasing availability of data from various sources, the traditional analytical methods may not be conducive anymore for examining clinical outcomes. Machine‐learning approaches, which are algorithms trained to identify patterns in large data sets, are ideally suited to facilitate data‐driven decision making. The field of orthodontics is particularly ripe for embracing the big data analytics platform to improve decision making in clinical practice. The availability of omics data, state‐of‐the‐art imaging and potential for establishing large clinical data repositories have favourably positioned the specialty of orthodontics to deliver personalized and precision orthodontic care. Specifically, we discuss about next‐generation sequencing, radiomics in the context of CBCT imaging, and how centralized data repositories can enable real‐time data pooling from multiple sources.

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Objectives In 2017, the state of Illinois changed the system by which they define severe malocclusion from the DentaQuest Orthodontic Criteria Index to the Handicapping Labio‐Lingual Deviation Index (HLD). The purpose of this study was to compare subjects who were submitted for coverage under either the DentaQuest Orthodontic Criteria Index or the HLD index to see if a difference exists in the number of subjects who received coverage and the type of malocclusions that were covered. Methods All subjects evaluated for orthodontic coverage by the Illinois Department of Human Services for treatment at the University of Illinois, College of Dentistry during the years 2016 and 2017 were included in this study. One hundred consecutively approved and 100 consecutively denied subjects from both 2016 and 2017 were selected for further analysis. Results There was a statistically significant decrease in the overall rate of approval in 2017 compared to 2016. No difference was found in the approval rate of Class I, II, or III subjects, but there was a significant decrease in the approval rate of subjects with impacted teeth. Conclusions The implementation of the HLD index has significantly decreased access to orthodontic care for Medicaid patients in Illinois.