Int J Stomatol

Int J Stomatol ›› 2026, Vol. 53 ›› Issue (5): 631-638.doi: 10.7518/gjkq.2026616

• Digital Oral Medicine Column • Previous Articles    

Applications of artificial intelligence in the diagnosis of endodontal diseases

Wanling Cai(),Qinghua Zheng,Lan Zhang,Dingming Huang()   

  1. State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Received:2025-04-22 Revised:2026-02-26 Online:2026-09-01 Published:2026-08-28
  • Contact: Dingming Huang E-mail:cwl18048406627@163.com;dingminghuang@163.com
  • Supported by:
    Chengdu Key R&D Support Plan(2024-YF05-02478-SN);Sichuan Province Science and Technology Program(2024NSFSC0593)

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Abstract:

In recent years, the rapid development of artificial intelligence (AI) technology has led to a steady rise in its application in the field of endodontics. This review focuses on evaluating the latest advancements of AI in the diagnosis of endodontic diseases, including caries, non-carious diseases of dental hard tissues, and periapical lesions. It discusses the advantages of AI-assisted diagnosis and reviews the limitations and challenges posed by dataset quality, transparency of AI, and potential ethical dilemmas. In the near future, AI is expected to significantly influence the daily workflow, diagnosis, and treatment strategies of dental and pulp diseases.

Key words: artificial intelligence, endodontal diseases, diagnosis

CLC Number: 

  • R781.05

TrendMD: 

Tab 1

AI-assisted diagnosis of caries based on intraoral photograph"

作者年份成像设备样本量学习模型评价结果
Khan等[11]2022多类型609CNNDental-Net、VGG-16、MobileNetV2、InceptionV3、ResNet-18的准确率分别为91.09%、87.76%、84.55%、82.65%、86.78%
Park等[12]2022口内摄像机2 348CNNResNet-18、Faster R-CNN的准确率分别为81.3%、89.0%
Kühnisch等[13]2022专业摄像机2 417CNN敏感度89.6%，精确度93.6%，特异性94.3%，准确率92.5%
Sonavane等[14]2021-74CNN准确率71.4%
Liu等[15]2020口内摄像机12 600CNN敏感度97.7%，精确度91.3%，特异性92.9%，准确率95.0%

Tab 2

AI-assisted diagnosis of caries based on two-dimensional radiograph"

作者年份样本量X线片类型学习模型评价结果
Liu等[18]20244 278根尖片CNN敏感度89%，特异性89%，准确率88%
Dhanak等[19]2024100咬翼片ANN敏感度75%，精确度85%
Estai等[20]20222 468咬翼片CNN敏感度89%，特异性86%，准确率87%
Bayraktar等[24]20221 000咬翼片CNN敏感度72%，特异性98%，准确率94.6%
Chen等[25]2022978咬翼片CNN敏感度72%，特异性93%，准确率87%
Zhu等[21]20221 159全景片CNN敏感度86%，精确度94.09%，准确率93.61%
Chen等[23]20221 100全景片CNN敏感度82.3%，精确度74.1%，F1分数78.0%
Vinayahalingam等[22]2021400全景片CNN敏感度86%，特异性88%，准确率87%

Tab 3

AI-assisted diagnosis of periapical periodontitis based on radiograph"

作者年份成像类型学习模型样本量评价结果
Shafi等[41]2023根尖片CNN534准确率98%，精确度76%，敏感度75%
Li等[42]2021根尖片CNN460准确率93%，特异性90%，敏感度95%，精确度92%
Chen等[44]2021根尖片CNN2 900精确度52%，敏感度52%
Calazans等[43]2022CBCTCNN885准确率70%，精确度76%，特异性76%
Kirnbauer等[45]2022CBCTCNN144敏感度97%，特异性88%
Endres等[46]2020全景片CNN3 099阳性预测值67%，F1分数58%，曲线下面积60%
Ekert等[47]2019全景片CNN2 579曲线下面积85%，敏感度65%，特异性87%
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