Digital recognition of color alteration in gingiva using a convolutional neural network.
TL;DR
Differentiation between GI scores of 0 and 1 using CNNs provides an objective method for detecting gingival color alterations and has potential for improving patients' awareness of periodontal health, enhancing motivation to maintain oral hygiene, and encouraging dental visits.
Abstract
Background
Gingivitis is a common condition in individuals with inadequate oral hygiene. Although dental indices are widely used to assess periodontal status and educate patients, digital technologies have recently been incorporated into this field.
Objectives
The aim of the study was to provide an objective digital method to resolve the ambiguity between the first two scores of the gingival index (GI): absence of color alteration (score 0); and presence of color alteration in the gingiva (score 1), by designing a convolutional neural network (CNN) algorithm capable of detecting gingival color changes with high accuracy. MATERIAL AND
Methods
In this cross-sectional study, 10 CNN models were developed and trained to distinguish between normal and abnormal gingival color. A total of 6,660 augmented, pre-classified frontalview images of the patients' gingiva were included. The dataset was divided into a training set (4,640 images; 70%) to train the CNN models and a test set (2,020 images; 30%) to test them. All images were previously classified by periodontists as representing either normal (0) or abnormal (1) gingival color. During training, the CNN models learned to classify gingival color based on these reference labels. Model performance was subsequently evaluated in the test phase by comparing the models' predictions with the reference diagnoses provided by periodontists.
Results
To evaluate inter-rater reliability, model performance was examined using Cohen's kappa coefficient and classification accuracy relative to the reference diagnoses established by the periodontists. Model 4 demonstrated excellent inter-rater reliability (κ = 1.00) and achieved 99.9% accuracy, followed by Model 8 (99.7%) and Model 5 (99.3%). Models 7 and 3 showed lower accuracies of 81.1% and 88.1%, respectively. All models demonstrated statistically significant agreement with the reference diagnoses (p < 0.001).
Conclusions
Differentiation between GI scores of 0 and 1 using CNNs provides an objective method for detecting gingival color alterations. This approach has potential for improving patients' awareness of periodontal health, enhancing motivation to maintain oral hygiene, and encouraging dental visits.