{"id":31060,"date":"2026-06-29T08:52:08","date_gmt":"2026-06-29T13:52:08","guid":{"rendered":"https:\/\/www.aae.org\/specialty\/?p=31060"},"modified":"2026-06-26T14:53:54","modified_gmt":"2026-06-26T19:53:54","slug":"smarter-radiographs-ai-applications-in-2d-and-3d-endodontic-imaging","status":"publish","type":"post","link":"https:\/\/www.aae.org\/specialty\/smarter-radiographs-ai-applications-in-2d-and-3d-endodontic-imaging\/","title":{"rendered":"Smarter Radiographs: AI Applications in 2D and 3D Endodontic Imaging"},"content":{"rendered":"

\"\"<\/p>\n

By Omid Dianat, DDS, MS<\/em><\/p>\n

Artificial intelligence (AI) has integrated into human life faster than almost any prior technology. ChatGPT reached 100 million users in two months, a pace that took the telephone 75 years. The FDA has now cleared more than 1,300 AI-enabled medical devices, and healthcare AI investment is projected to grow from $37 billion to over $600 billion this decade. Endodontics, with its heavy reliance on structured imaging data, sits at the epicenter of this transformation.<\/p>\n

Seeing What We Could Not See Before<\/strong><\/p>\n

The most mature endodontic AI application is segmentation: teaching a network to outline anatomical structures on CBCT, voxel by voxel. U-Net architectures trained with micro-CT-derived ground truth can now delineate the tooth, pulp cavity, pulp chamber, and root canals with Dice similarity coefficients up to 96 percent (1). In maxillary premolars, automated pulp cavity segmentation has reached DSC values of 88 to 93 percent while running 75 times faster than manual methods (2).<\/p>\n

Root canal morphology, one of the most persistent clinical challenges, is another area where AI is making progress. Deep learning classifiers can now identify C-shaped canals in mandibular second molars with an AUC as high as 0.99 on periapical radiographs (3), a performance that has been externally validated across four datasets from multiple institutions (4). Separately, on panoramic radiographs, ensemble CNN models can predict root number in maxillary premolars with an AUC of 0.94, matching an experienced endodontist (5).<\/p>\n

Sharpening Conventional CBCT: The Move Toward Virtual Micro-CT<\/h2>\n

While CBCT provides essential 3D information, its spatial resolution is often limited by radiation dose and detector constraints. Deep learning \u201csuper-resolution\u201d is beginning to close that gap. Recent micro-CT-guided neural networks have demonstrated the ability to enhance standard CBCT slices to reveal accessory canals and isthmuses that are invisible at conventional resolutions (6, 7). This technology suggests a future where we can extract near-micro-CT detail from a standard patient scan.<\/p>\n

Diagnostic Performance and Clinical Reality<\/strong><\/p>\n

Periapical lesion detection on 2D radiographs is where most researchers have focused. AI models have reached a plateau of high performance, with pooled sensitivity values between 0.92 and 0.94 (8). In practical terms, current AI models can reliably identify periapical radiolucencies on conventional radiographs, though performance varies with image type and ground truth definitions.<\/p>\n

A recent randomized controlled trial put this to a real-world test. Thirty dentists evaluated 50 panoramic radiographs in a crossover design, with and without AI assistance. Overall diagnostic accuracy improved from 91.6% to 93.3% (p < 0.001). Junior clinicians gained the most from this, and AI shifted treatment decisions toward more conservative approaches (9).<\/p>\n

Meanwhile, independent evaluation of commercial CBCT platforms reveals a critical nuance: while high sensitivity (93.9%) makes AI an excellent tool for ruling out disease (fewer missed lesions), moderate specificity (65.2%) and performance drops in root-filled teeth introduce the risk of false positives (10). These findings tell us an important point: Current AI tools tend to be stronger at identifying disease on CBCT than at ruling out false positives, and performance varies substantially across clinical contexts. The reason might be simple: AI platforms give back what they have been fed, so if we haven’t trained one AI system to identify internal root resorption, it simply will not!<\/p>\n

The Path Forward<\/h2>\n

Newer training paradigms are expanding what is possible with limited data. Self-supervised learning methods, which learn visual representations from unlabeled images before fine-tuning on a small, labeled set, have achieved 84% accuracy for external cervical resorption detection on periapical radiographs (11). This matters because labeled endodontic datasets are expensive and scarce. Self-supervised methods that reduce our dependence on them will accelerate our progress in the field.<\/p>\n

Limitations and Conclusion<\/h2>\n

The evidence so far is encouraging but incomplete. Most published studies rely on small, single-center datasets, and annotation quality varies widely. AI in endodontic imaging needs more multi-center data collection, more open-source models, and a shift from simply training models toward studies that measure endodontic outcomes using different AI tools.<\/p>\n

In conclusion, AI will not replace endodontists; it is a pattern recognition instrument that, when properly validated, can make us faster, more consistent, and more confident. The technology is no longer a prospective matter; it is a fast-paced reality. The question for our specialty is not whether to engage with it, but how to do so responsibly.<\/p>\n

References<\/h2>\n
    \n
  1. Lin X, Fu Y, Ren G, et al. Micro-computed tomography-guided artificial intelligence for pulp cavity and tooth segmentation on cone-beam computed tomography. J Endod. 2021;47(12):1933-1941.<\/li>\n
  2. Santos-Junior AO, Fontenele RC, Sampaio Neves F, et al. A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT. Sci Rep. 2025;15:5509.<\/li>\n
  3. Yang S, Lee H, Jang B, et al. Development and validation of a visually explainable deep learning model for classification of C-shaped canals of the mandibular second molars. J Endod. 2022;48(7):914-921.<\/li>\n
  4. Yang S, Kim KD, Kise Y, et al. External validation of the effect of combined use of object detection for C-shaped canal classification: a multicenter study. J Endod. 2024;50(5):627-636.<\/li>\n
  5. Azgari E, Azgari C, Sazak Ovecoglu H. Deep learning-based detection of root numbers in maxillary premolars. Int Endod J. 2026. doi:10.1111\/iej.70091.<\/li>\n
  6. Nazari A, Najafi SMY, Abbasi R, et al. Deep learning for dentomaxillofacial CBCT image quality enhancement: a pilot study. Imaging Sci Dent. 2025;55(3):271-279.<\/li>\n
  7. Chen P, Shen B, Yang Y, et al. Deep learning super-resolution for dental CBCT using micro-CT reference and edge loss function. J Dent. 2026;164:106209.<\/li>\n
  8. Sadr S, Mohammad-Rahimi H, Motamedian SR, et al. Deep learning for detection of periapical radiolucent lesions: a systematic review and meta-analysis of diagnostic test accuracy. J Endod. 2023;49(3):248-261.<\/li>\n
  9. Pul U, Tichy A, Pitchika V, Schwendicke F. Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: a randomized controlled trial. J Dent. 2025;160:105868.<\/li>\n
  10. Allihaibi M, Koller G, Mannocci F. Diagnostic accuracy of an artificial intelligence-based platform in detecting periapical radiolucencies on CBCT scans of molars. J Dent. 2025;160:105854.<\/li>\n
  11. Mohammad-Rahimi H, Dianat O, Abbasi R, et al. Artificial intelligence for detection of external cervical resorption using label-efficient self-supervised learning method. J Endod. 2024;50(2):144-153.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    By Omid Dianat, DDS, MS Artificial intelligence (AI) has integrated into human life faster than almost any prior technology. 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