Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

Orthodontically induced root resorption (OIRR) is difficult to assess accurately using traditional 2D imaging due to distortion and low sensitivity. While CBCT offers more precise 3D evaluation, manual segmentation remains labor-intensive and prone to variability. Recent advances in deep learning enable automatic, accurate tooth segmentation from CBCT images. This study applies deep learning and CBCT technology to quantify OIRR and analyze its risk factors, aiming to improve assessment accuracy, efficiency, and clinical decision-making. This study retrospectively analyzed CBCT scans of 108 orthodontic patients to assess OIRR using deep learning-based tooth segmentation and volumetric analysis. Statistical analysis was performed using linear regression to evaluate the influence of patient-related factors. A significance level of p < 0.05 was considered statistically significant. Root volume significantly decreased after orthodontic treatment (p < 0.001). Age, gender, open (deep) bite, severe crowding, and other factors significantly influenced root resorption rates in different tooth positions. Multivariable regression analysis showed these factors can predict root resorption, explaining 3% to 15.4% of the variance. This study applied a deep learning model to accurately assess root volume changes using CBCT, revealing significant root volume reduction after orthodontic treatment. It found that underage patients experienced less root resorption, while factors like anterior open bite and deep overbite influenced resorption in specific teeth, though skeletal pattern, overjet, and underbite were not significant predictors.

The dental follicle (DF) plays an indispensable role in tooth eruption by regulating bone remodeling through their influence on osteoblast and osteoclast activity. The process of tooth eruption involves a series of intricate regulatory mechanisms and signaling pathways. Disruption of the parathyroid hormone‑related protein (PTHrP) in the PTHrP‑PTHrP receptor signaling pathway inhibits osteoclast differentiation by DF cells (DFCs), thus resulting in obstructed tooth eruption. Furthermore, parathyroid hormone receptor‑1 mutations are linked to primary tooth eruption failure. Additionally, the Wnt/β‑catenin, TGF‑β, bone morphogenetic protein and Hedgehog signaling pathways have crucial roles in DFC involvement in tooth eruption. DFC signal loss or alteration inhibits osteoclast differentiation, affects osteoblast and cementoblast differentiation, and suppresses DFC proliferation, thus resulting in failed tooth eruptions. Abnormal tooth eruption is also associated with a range of systemic syndromes and genetic diseases, predominantly resulting from pathogenic gene mutations. Among these conditions, the following disorders arise due to genetic mutations that disrupt DFCs and impede proper tooth eruption: Cleidocranial dysplasia associated with Runt‑related gene 2 gene mutations; osteosclerosis caused by CLCN7 gene mutations; mucopolysaccharidosis type VI resulting from arylsulfatase B gene mutations; enamel renal syndrome due to FAM20A gene mutations; and dentin dysplasia caused by mutations in the VPS4B gene. In addition, regional odontodysplasia and multiple calcific hyperplastic DFs are involved in tooth eruption failure; however, they are not related to gene mutations. The specific mechanism for this effect requires further investigation. To the best of our knowledge, previous reviews have not comprehensively summarized the syndromes associated with DF abnormalities manifesting as abnormal tooth eruption. Therefore, the present review aims to consolidate the current knowledge on DFC signaling pathways implicated in abnormal tooth eruption, and their association with disorders of tooth eruption in genetic diseases and syndromes, thereby providing a valuable reference for future related research.