Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs.

PURPOSE: The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. MATERIALS AND METHODS: A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). RESULTS: The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. CONCLUSIONS: Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. CLINICAL SIGNIFICANCE: The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.

A systematic review of the accuracy and efficiency of dental movements with Invisalign®.

We are currently living in an era where the use of computer-aided design/computer-aided manufacturing has allowed individualized orthodontic treatments, but has also incorporated enhanced digitalized technology that does not permit improvisation. The purpose of this systematic review was to analyze publications that assessed the accuracy and efficiency of the Invisalign® system. A systematic review was performed using a search strategy to identify articles that referenced Invisalign®, which were published between August 2007 and August 2017, and listed in the following databases: MEDLINE, Embase, Cochrane Library, Web of Knowledge, Google Scholar, and LILACS. Additionally, a manual search of clinical trials was performed in scientific journals and other databases. To rate the methodological quality of the articles, a grading system described by the Swedish Council on Technology Assessment in Health Care was used, in combination with the Cochrane tool for risk of bias assessment. We selected 20 articles that met the inclusion criteria and excluded 5 due to excess biases. The level of evidence was high. Although it is possible to treat malocclusions with plastic systems, the results are not as accurate as those achieved by treatment with fixed appliances.