Background: The new classification of periodontal diseases introduced a new set of rules for periodontal diagnosis. The objective of this study was to develop and test the implementation of a mobile device application for periodontal diagnosis. Material and Methods: An integral algorithm that included periodontal health / related conditions and periodontitis was developed based on the classification of periodontal diseases of 2018. A mobile application for Android implementing the algorithm was developed using the framework MIT App Inventor. Once the app was debugged for glitches and performance of the algorithm, it was tested with 20 voluntary dental students, postgraduate students of periodontology, and professors in an academic setting. Participants were asked to determine the diagnosis of 10 predetermined clinical cases using two strategies: diagnosis based on knowledge and with the PerioSmart app. The results were tabulated, and the concordance rate was calculated. Results: In general, the use of the PerioSmart application had a better concordance rate than diagnosis based on knowledge. In particular, the mobile app was better in determining the type of diagnosis, stage/grade of periodontitis, and with better efficiency. Conclusions: The mobile device application demonstrated efficiency and good concordance rate and therefore can improve the periodontal diagnosis. Key words:Mobile application, periodontal diagnosis, periodontitis, gingivitis. Copyright:
Background: The new classification of periodontal diseases introduced a new set of rules for periodontal diagnosis. The objective of this study was to develop and test the implementation of a mobile device application for periodontal diagnosis. Material and Methods: An integral algorithm that included periodontal health / related conditions and periodontitis was developed based on the classification of periodontal diseases of 2018. A mobile application for Android implementing the algorithm was developed using the framework MIT App Inventor. Once the app was debugged for glitches and performance of the algorithm, it was tested with 20 voluntary dental students, postgraduate students of periodontology, and professors in an academic setting. Participants were asked to determine the diagnosis of 10 predetermined clinical cases using two strategies: diagnosis based on knowledge and with the PerioSmart app. The results were tabulated, and the concordance rate was calculated. Results: In general, the use of the PerioSmart application had a better concordance rate than diagnosis based on knowledge. In particular, the mobile app was better in determining the type of diagnosis, stage/grade of periodontitis, and with better efficiency. Conclusions: The mobile device application demonstrated efficiency and good concordance rate and therefore can improve the periodontal diagnosis. Key words:Mobile application, periodontal diagnosis, periodontitis, gingivitis. Copyright:
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