Modified Gingival Index (MGI) Classification Using Dental Selfies

Abstract

Background: Gum diseases are prevalent in a large proportion of the population worldwide. Unfortunately, most people do not follow a regular dental checkup schedule, and only seek treatment when experiencing acute pain. We aim to provide a system for classifying gum health status based on the MGI (Modified Gingival Index) score using dental selfies alone. Method: The input to our method is a manually cropped tooth image and the output is the MGI classification of gum health status. Our method consists of a cascade of two stages of robust, accurate, and highly optimized binary classifiers optimized per tooth position. Results: Dataset constructed from a pilot study of 44 participants taking dental selfies using our iGAM app. From each such dental selfie, eight single-tooth images were manually cropped, producing a total of 1520 images. The MGI score for each image was determined by a single examiner dentist. On a held-out test-set our method achieved an average AUC (Area Under the Curve) score of 95%. Conclusion: The paper presents a new method capable of accurately classifying gum health status based on the MGI score given a single dental selfie. Enabling personal monitoring of gum health—particularly useful when face-to-face consultations are not possible.