A deep-learning system for dental plaque quantification from disclosed intraoral photographs, validation against clinical plaque scoring.

OBJECTIVES

To determine whether buccal plaque coverage measured automatically from disclosed intraoral photographs agrees with the Turesky-modified Quigley-Hein plaque index (T-QHPI) scored by calibrated dentists.

METHODS

Twenty adults were photographed in one frontal and two lateral views after 48 hours without oral hygiene, and a calibrated dentist, blind to the model, scored the T-QHPI on the same teeth. Two U-Net (ResNet-34) models, one per view, were trained under participant-level five-fold cross-validation, and coverage was the stained percentage of each outlined buccal crown. Coverage was compared with the clinical grade by Spearman's ρ and, after conversion to T-QHPI grades under index-definition and calibrated thresholds, by weighted κ; participant means by Pearson's r, the intraclass correlation coefficient ICC(2,1) and Bland-Altman analysis.

RESULTS

438 teeth were compared with the clinical grade. Segmentation Dice was 0.88 in both views. Coverage ranked teeth with the clinical index at ρ = 0.856 (95% CI 0.821 to 0.877). After calibration, weighted κ was 0.812 (0.766 to 0.844) against 0.791 (0.710 to 0.853) between two examiners, and agreement within one grade was 96.8%. Participant means correlated at r = 0.930, with ICC(2,1) 0.916.

CONCLUSIONS

Automated buccal plaque coverage from disclosed photographs agreed with the T-QHPI scored by a calibrated examiner, and after threshold calibration the agreement did not differ from that between two examiners.

CLINICAL SIGNIFICANCE: Automated plaque grading from disclosed photographs could serve as an auxiliary tool, a second opinion that supports, rather than replaces, the clinician's plaque scoring, pending external validation.