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<p>Differentiating between endodontic and non-endodontic sources of orofacial pain is a diagnostic challenge owing to overlapping clinical presentations. Artificial intelligence (AI) offers a potential solution to improve the diagnostic accuracy of endodontics by analysing complex clinical and radiographic data. To evaluate the performance of an artificial intelligence (AI) model in distinguishing endodontic pain from non-endodontic pain and compare its diagnostic accuracy, sensitivity, and specificity with those of an expert panel. This cross-sectional diagnostic study included 224 participants (134 with endodontic pain and 90 without endodontic pain). Clinical and radiographic data were analysed using a random forest AI model trained on features including radiographic lesions, pain characteristics, and pulp vitality tests. Model performance metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC-ROC), were assessed across the training, validation, and test datasets. The model’s performance was compared to that of an expert panel using statistical tests. The AI model demonstrated an overall diagnostic accuracy of 91.8% on the test set, with sensitivity and specificity of 92.1% and 91.4%, respectively. The AI model showed significantly higher sensitivity (p = 0.03) than the expert panel (accuracy: 89.2%). Feature importance analysis identified radiographic lesions (34%), pain characteristics (28%), and pulp vitality test results (25%) as the most influential predictors. The AI model exhibited superior diagnostic accuracy compared with the expert panel, indicating its potential as a reliable tool for differentiating endodontic pain from non-endodontic sources. Incorporating AI into clinical workflows can enhance diagnostic precision and improve patient outcomes in endodontics. </p>

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Development and validation of a predictive AI model for differential diagnosis of endodontic and non-endodontic orofacial pain: a comparative study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Mohmed Isaqali Karobari,
  • P. J. Nagarathna,
  • Santosh R. Patil,
  • Niher Tabassum Snigdha,
  • Ankita Mathur,
  • Mohammed Sharique Ahmed Quadri,
  • Mohammad Fareed

摘要

Differentiating between endodontic and non-endodontic sources of orofacial pain is a diagnostic challenge owing to overlapping clinical presentations. Artificial intelligence (AI) offers a potential solution to improve the diagnostic accuracy of endodontics by analysing complex clinical and radiographic data. To evaluate the performance of an artificial intelligence (AI) model in distinguishing endodontic pain from non-endodontic pain and compare its diagnostic accuracy, sensitivity, and specificity with those of an expert panel. This cross-sectional diagnostic study included 224 participants (134 with endodontic pain and 90 without endodontic pain). Clinical and radiographic data were analysed using a random forest AI model trained on features including radiographic lesions, pain characteristics, and pulp vitality tests. Model performance metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC-ROC), were assessed across the training, validation, and test datasets. The model’s performance was compared to that of an expert panel using statistical tests. The AI model demonstrated an overall diagnostic accuracy of 91.8% on the test set, with sensitivity and specificity of 92.1% and 91.4%, respectively. The AI model showed significantly higher sensitivity (p = 0.03) than the expert panel (accuracy: 89.2%). Feature importance analysis identified radiographic lesions (34%), pain characteristics (28%), and pulp vitality test results (25%) as the most influential predictors. The AI model exhibited superior diagnostic accuracy compared with the expert panel, indicating its potential as a reliable tool for differentiating endodontic pain from non-endodontic sources. Incorporating AI into clinical workflows can enhance diagnostic precision and improve patient outcomes in endodontics.