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July 5, 2026Journal of Clinical Medicine0 citationsOpen Access

Artificial Intelligence in Radiographic Diagnosis of Peri-Implantitis

Artificial Intelligence for Radiographic Diagnosis of Peri-Implantitis: A Comprehensive Review on Detection, Measurement, and Risk Stratification

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Authors

FFFrancesco FanelliATAngela TisciLMLorenzo Lo Muzio

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Overview

Comprehensive review synthesizes evidence on AI's role in detecting, measuring, and assessing peri-implantitis risk.

Key Points

  • This review synthesizes evidence on the effectiveness of AI in diagnosing peri-implantitis and measuring bone loss.
  • Examined original studies published from 2013 to 2025 focusing on AI applications in peri-implantitis.
  • Included data extraction on imaging modalities, AI models, performance metrics, and clinical relevance.
  • Performed qualitative synthesis of findings from eleven eligible studies.
  • Ten studies were included after excluding one due to retrieval issues.
  • Most studies used periapical/intraoral radiographs for assessing marginal bone loss; few utilized panoramic imaging.
  • AI performance showed promise for detecting bone loss and classifying severity, but studies lacked external validation.

Cite This Study

Fanelli et al. (2026) studied this question.

synapsesocial.com/papers/6a49f6c9f5d1d45b288010efhttps://doi.org/10.3390/jcm15135210
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