AI-Driven Detection and Measurement of Keratinized Gingiva in Dental Photographs: Validation Using Reference Retainers

Abstract

Aim: To evaluate a deep learning (DL) model for detecting keratinized gingiva (KG) in dental photographs and validate its clinical applicability using reference retainers for calibration. Materials and Methods: A total of 576 sextant photographs were selected from 32 subjects, each with three sets of photographs: iodine-stained, unstained and line-marked retainers. Relative keratinized gingiva width (rKGW) was measured using visual, functional and histochemical staining methods with reference retainers. A pre-trained DeepLabv3 model with ResNet50 backbone was fine-tuned to predict KG areas, which were then applied to the photographs with line-marked retainers for subsequent rKGW measurement. Results: The AI model achieved a Dice coefficient of 93.30% and an accuracy of 93.32%. Using histochemical measurements as gold standards, the absolute differences in rKGW of AI measurements were statistically insignificant with visual (p = 0.935) and functional (p = 0.979) measurements. The adjusted difference between AI and histochemical measurements was 0.377 mm. AI closely matched histochemical measurements in the maxillary anterior region (0.011 mm, p = 0.903) but was significantly higher in the maxillary posterior region (0.327 mm, p < 0.05). Conclusions: The proposed AI model is the first to reliably identify full-mouth KG, validated thoroughly using reference retainers. However, predictions for posterior teeth warrant further improvement.