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<p>Artificial intelligence (AI) has shown promise in dental imaging, yet its reliability for comprehensive diagnostic charting from panoramic radiographs (PAN) remains uncertain. We evaluated the diagnostic performance of a commercial AI platform (Diagnocat™, San Francisco, USA) in detecting common dental treatment features on PAN images. In this retrospective study, 147 patients (4,148 teeth) were analyzed against the consensus of two experienced readers. Tooth-level performance was assessed for missing teeth, fillings, crowns, pontics, endodontic treatments, orthodontic appliances, and implants, using patient-clustered nonparametric bootstrapping used to account for within-patient correlations. At the tooth level, the AI achieved high diagnostic accuracy across features (94.9–99.9%), with nearly perfect results for missing teeth, crowns, pontics, and implants. However, under a stringent patient-level “perfect match” criterion requiring error-free full-mouth reports, the AI succeeded in only 56.5% of cases (95% CI, 48.4–64.2%). Errors were most often related to fillings (57.3%) and endodontic treatments (19.5%). These findings highlight a critical gap between high per-tooth accuracy and clinically meaningful patient-level performance. These findings underscore that while the AI performs strongly at the tooth level, its patient-level performance is insufficient for autonomous diagnostic charting.</p>

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Detection accuracy of an AI platform for dental treatment features on panoramic radiographs – tooth- and patient-level analyses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Natalia Kazimierczak,
  • Nora Sultani,
  • Natalia Chwarścianek,
  • Szymon Krzykowski,
  • Joanna Janiszewska-Olszowska,
  • Zbigniew Serafin,
  • Wojciech Kazimierczak

摘要

Artificial intelligence (AI) has shown promise in dental imaging, yet its reliability for comprehensive diagnostic charting from panoramic radiographs (PAN) remains uncertain. We evaluated the diagnostic performance of a commercial AI platform (Diagnocat™, San Francisco, USA) in detecting common dental treatment features on PAN images. In this retrospective study, 147 patients (4,148 teeth) were analyzed against the consensus of two experienced readers. Tooth-level performance was assessed for missing teeth, fillings, crowns, pontics, endodontic treatments, orthodontic appliances, and implants, using patient-clustered nonparametric bootstrapping used to account for within-patient correlations. At the tooth level, the AI achieved high diagnostic accuracy across features (94.9–99.9%), with nearly perfect results for missing teeth, crowns, pontics, and implants. However, under a stringent patient-level “perfect match” criterion requiring error-free full-mouth reports, the AI succeeded in only 56.5% of cases (95% CI, 48.4–64.2%). Errors were most often related to fillings (57.3%) and endodontic treatments (19.5%). These findings highlight a critical gap between high per-tooth accuracy and clinically meaningful patient-level performance. These findings underscore that while the AI performs strongly at the tooth level, its patient-level performance is insufficient for autonomous diagnostic charting.