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.