Accuracy and Time Efficiency of Artificial Intelligence-Driven Tooth Segmentation on CBCT Images: A Validation Study Using Two Implant Planning Software Programs

IF 5.3 1区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Clinical Oral Implants Research Pub Date : 2025-07-18 DOI:10.1111/clr.70003

Panagiotis Ntovas, Piyarat Sirirattanagool, Praewvanit Asavanamuang, Shruti Jain, Lorenzo Tavelli, Marta Revilla-León, Maria Eliza Galarraga-Vinueza

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Abstract

Objectives

To assess the accuracy and time efficiency of manual versus artificial intelligence (AI)-driven tooth segmentation on cone-beam computed tomography (CBCT) images, using AI tools integrated within implant planning software, and to evaluate the impact of artifacts, dental arch, tooth type, and region.

Materials and Methods

Fourteen patients who underwent CBCT scans were randomly selected for this study. Using the acquired datasets, 67 extracted teeth were segmented using one manual and two AI-driven tools. The segmentation time for each method was recorded. The extracted teeth were scanned with an intraoral scanner to serve as the reference. The virtual models generated by each segmentation method were superimposed with the surface scan models to calculate volumetric discrepancies.

Results

The discrepancy between the evaluated AI-driven and manual segmentation methods ranged from 0.10 to 0.98 mm, with a mean RMS of 0.27 (0.11) mm. Manual segmentation resulted in less RMS deviation compared to both AI-driven methods (CDX; BSB) (p < 0.05). Significant differences were observed between all investigated segmentation methods, both for the overall tooth area and each region, with the apical portion of the root showing the lowest accuracy (p < 0.05). Tooth type did not have a significant effect on segmentation (p > 0.05). Both AI-driven segmentation methods reduced segmentation time compared to manual segmentation (p < 0.05).

Conclusions

AI-driven segmentation can generate reliable virtual 3D tooth models, with accuracy comparable to that of manual segmentation performed by experienced clinicians, while also significantly improving time efficiency. To further enhance accuracy in cases involving restoration artifacts, continued development and optimization of AI-driven tooth segmentation models are necessary.

Abstract Image

查看原文本刊更多论文

人工智能驱动的CBCT图像牙齿分割的准确性和时效性：两种种植规划软件的验证研究。

目的评估人工与人工智能（AI）驱动的锥形束计算机断层扫描（CBCT）图像分割的准确性和时间效率，使用人工智能工具集成在种植体计划软件中，并评估伪影、牙弓、牙齿类型和区域的影响。材料与方法本研究随机选择14例接受CBCT扫描的患者。利用采集到的数据集，使用一种人工工具和两种人工智能驱动工具对67颗提取的牙齿进行分割。记录每种方法的分割时间。拔牙后用口腔内扫描仪扫描作为参考。将每种分割方法生成的虚拟模型与表面扫描模型叠加，计算体积差异。结果人工智能与人工分割方法的差异范围为0.10 ~ 0.98 mm，平均RMS为0.27 (0.11)mm。人工分割方法的RMS偏差小于人工智能和人工智能分割方法(CDX；BSB) （p 0.05）。与人工分割相比，两种人工智能驱动的分割方法都缩短了分割时间（p < 0.05）。结论ai驱动的牙体分割可以生成可靠的虚拟三维牙体模型，其精度可与临床经验丰富的临床医生手工分割相媲美，同时显著提高了时间效率。为了进一步提高涉及修复工件的情况下的准确性，需要继续开发和优化人工智能驱动的牙齿分割模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Oral Implants Research 医学-工程：生物医学

CiteScore

7.70

自引率

11.60%

发文量

149

审稿时长

3 months

期刊介绍： Clinical Oral Implants Research conveys scientific progress in the field of implant dentistry and its related areas to clinicians, teachers and researchers concerned with the application of this information for the benefit of patients in need of oral implants. The journal addresses itself to clinicians, general practitioners, periodontists, oral and maxillofacial surgeons and prosthodontists, as well as to teachers, academicians and scholars involved in the education of professionals and in the scientific promotion of the field of implant dentistry.