ArticleInternational dental journal2026
Impact of Generative AI-Enhanced Low-Dose Cone-Beam Computed Tomography on Diagnosis and Treatment Planning for Impacted Mandibular Third Molars.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current evidence of generative artificial intelligence specifically developed for dental and maxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2026Pooled it
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Letter to the Editor on Generative AI-Enhanced Low-Dose CBCT for Impacted Mandibular Third Molars.International dental journal · 2026Article
- Feasibility of Artificial Intelligence-Processed Low-Dose Cone-Beam Computed Tomography in Dental Imaging.Bioengineering (Basel, Switzerland) · 2026Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate whether generative artificial intelligence (Gen-AI) could significantly enhance the visibility of the mandibular canal (MC) and the periodontal ligament (PL) of mandibular third molars (M3Ms) on low-dose cone-beam computed tomography (CBCT) images compared to standard-dose images, and to assess its impact on clinical decision-making compared to standard- and low-dose CBCT.
methodsA total of 302 CBCT scans with 151 paired from 90 patients with impacted M3Ms were acquired using one standard-dose (333 mGy × cm2) and various low-dose (78-131 mGy × cm2) protocols. Gen-AI models (Pix2Pix, CycleGAN, and diffusion models) were trained using paired standard- and low-dose CBCT images, with the CycleGAN-based model demonstrating superior performance. Quantitative image quality was assessed using the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and root mean square error (RMSE). Three blinded clinicians, a general practitioner (GP), oral-maxillofacial surgeon (OMFS) and oral-maxillofacial radiologist (OMFR), evaluated the MC and PL visibility, M3M-MC proximity, root morphology, adjacent molar status, surgical approach, and referral decisions. Pairwise comparisons were performed using Wilcoxon signed rank test.
resultsThe quality of Gen-AI-enhanced low-dose CBCT was significantly improved, achieving higher PSNR, lower MAE, and lower RMAE compared to the original low-dose CBCT (all P < .001), and maintained excellent anatomical fidelity with an SSIM of 0.97 compared to standard-dose CBCT. Gen-AI-enhanced low-dose images showed significantly higher MC visibility for all clinicians and higher PL visibility for both the GP and OMFS compared to low-dose images. No significant differences were observed for other variables.
conclusionGen-AI-enhanced low-dose CBCT images significantly improved the visibility of the MC and PL for M3M evaluation. Compared to the original CBCTs, these AI-enhanced low-dose images did not significantly affect risk assessments, treatment strategies, or patient management decisions, and were largely indistinguishable from original images.
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