Impact of Generative AI-Enhanced Low-Dose Cone-Beam Computed Tomography on Diagnosis and Treatment Planning for Impacted Mandibular Third Molars.

ABSTRACT:

Objectives

To 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.

Methods

A 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. Quantitativ

SUBMITTER: Zhang R 

PROVIDER: S-EPMC12681551 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature