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.
Highlights
Our goal is to develop a suite of features to be evaluated, that we tailored single tooth images
As described in the method section, just 35 features were extracted from each image
Lateral incisor classifiers were more accurate than central incisor classifiers. These results demonstrate the reliability of our method, since they correlate with clinical dental findings that the gums of the lower teeth tend to show inflammation and escalation first, due to proximity to the salivary glands and poor brushing technique [33]
Summary
Gingivitis which goes untreated usually escalates to periodontitis, the irreversible stage of gum disease. 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. 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. 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— useful when face-to-face consultations are not possible
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