Evaluation of an Intraoral Camera with an AI-Based Application for the Detection of Gingivitis

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Evaluation shows low agreement between AI detection and clinical diagnosis of gingivitis, suggesting improvements needed for accuracy.

The camera-based application demonstrated poor reliability in detecting gingivitis, highlighting challenges in visual diagnosis.
Agreement between the application's output and bleeding on probing was low, with a kappa value of 0.055, indicating limited effectiveness.
Using standardized intraoral photographs processed by a machine-learning algorithm, 110 participants were included in the analysis.
Findings emphasize the need for more refined approaches in visual diagnostics for gingivitis and other oral health conditions.

Ehrensperger et al. (2025) studied this question.

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View Full Paper

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Authors

Evaluation shows low agreement between AI detection and clinical diagnosis of gingivitis, suggesting improvements needed for accuracy.

The camera-based application demonstrated poor reliability in detecting gingivitis, highlighting challenges in visual diagnosis.
Agreement between the application's output and bleeding on probing was low, with a kappa value of 0.055, indicating limited effectiveness.
Using standardized intraoral photographs processed by a machine-learning algorithm, 110 participants were included in the analysis.
Findings emphasize the need for more refined approaches in visual diagnostics for gingivitis and other oral health conditions.

Ehrensperger et al. (2025) studied this question.

View Full Paper

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Bookmark