ArticleBMC oral health2025
Deep learning for automated dental plaque index assessment: validation against expert evaluations.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Mapping dental biofilms: from plaque index through planimetry to volumetric analysis.Clinical oral investigations · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- The Expanding Role of Artificial Intelligence in Dentistry: A Cross-Specialty Chairside Perspective.Cureus · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThe integration of artificial intelligence (AI) into healthcare has led to promising advancements in clinical decision-making and diagnostic accuracy. In dentistry, automated methods to evaluate oral hygiene measures, such as dental plaque detection, could improve patient care and streamline remote assessments.
objectiveThis study aimed to develop and evaluate a deep learning (DL)-based system that automatically detects and quantifies dental plaque, using a standardized plaque index, from intraoral images.
methodsSeventy participants were assessed using the Quigley-Hein plaque index, a clinical measure of plaque accumulation, following the application of a plaque-disclosing agent. Images were captured before and after dye application. Each tooth was labeled using the LabelMe software, indicating both tooth number and plaque presence for training and validation of the DL model. The performance of the DL-based system was statistically compared to the assessments of a highly experienced dentist (10 years) and a dental hygienist (1 year).
resultsAfter data augmentation, the DL model achieved a micro-average accuracy of 73.67% and a macro-average accuracy of 65.15%, with a precision of 76.34%, recall of 65.15%, and an F1 score of 66.15%. Statistical analysis showed no significant difference between the DL model's performance and that of the experienced dentist (P > 0.05), supporting its clinical reliability.
conclusionThe DL-based system successfully automated the evaluation of dental plaque from images, performing comparably to an experienced clinician. These findings underscore the potential for AI-driven plaque assessment tools to enhance digital dentistry, potentially supporting remote dental evaluations and improve oral healthcare delivery.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.