Validation of a novel AI-based automated multimodal image registration of CBCT and intraoral scan aiding presurgical implant planning

IF 4.8 1区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Clinical Oral Implants Research Pub Date : 2024-08-05 DOI:10.1111/clr.14338

Bahaaeldeen M. Elgarba, Rocharles Cavalcante Fontenele, Saleem Ali, Abdullah Swaity, Jan Meeus, Sohaib Shujaat, Reinhilde Jacobs

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引用次数: 0

Abstract

Objectives

The objective of this study is to assess accuracy, time-efficiency and consistency of a novel artificial intelligence (AI)-driven automated tool for cone-beam computed tomography (CBCT) and intraoral scan (IOS) registration compared with manual and semi-automated approaches.

Materials and Methods

A dataset of 31 intraoral scans (IOSs) and CBCT scans was used to validate automated IOS-CBCT registration (AR) when compared with manual (MR) and semi-automated registration (SR). CBCT scans were conducted by placing cotton rolls between the cheeks and teeth to facilitate gingival delineation. The time taken to perform multimodal registration was recorded in seconds. A qualitative analysis was carried out to assess the correspondence between hard and soft tissue anatomy on IOS and CBCT. In addition, a quantitative analysis was conducted by measuring median surface deviation (MSD) and root mean square (RMS) differences between registered IOSs.

Results

AR was the most time-efficient, taking 51.4 ± 17.2 s, compared with MR (840 ± 168.9 s) and SR approaches (274.7 ± 100.7 s). Both AR and SR resulted in significantly higher qualitative scores, favoring perfect IOS-CBCT registration, compared with MR (p = .001). Additionally, AR demonstrated significantly superior quantitative performance compared with SR, as indicated by low MSD (0.04 ± 0.07 mm) and RMS (0.19 ± 0.31 mm). In contrast, MR exhibited a significantly higher discrepancy compared with both AR (MSD = 0.13 ± 0.20 mm; RMS = 0.32 ± 0.14 mm) and SR (MSD = 0.11 ± 0.15 mm; RMS = 0.40 ± 0.30 mm).

Conclusions

The novel AI-driven method provided an accurate, time-efficient, and consistent multimodal IOS-CBCT registration, encompassing both soft and hard tissues. This approach stands as a valuable alternative to manual and semi-automated registration approaches in the presurgical implant planning workflow.

Abstract Image

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验证基于人工智能的新型 CBCT 和口腔内扫描自动多模态图像配准技术，帮助进行术前种植规划。

研究目的本研究旨在评估一种新型人工智能（AI）驱动的锥形束计算机断层扫描（CBCT）和口腔内扫描（IOS）自动配准工具与手动和半自动方法相比的准确性、时间效率和一致性：31个口内扫描（IOS）和CBCT扫描数据集用于验证IOS-CBCT自动配准（AR）与手动（MR）和半自动配准（SR）的比较。进行 CBCT 扫描时，在脸颊和牙齿之间放置棉卷，以方便划定牙龈。以秒为单位记录进行多模态配准所需的时间。我们进行了定性分析，以评估 IOS 和 CBCT 上硬组织和软组织解剖之间的对应关系。此外，还通过测量已登记 IOS 之间的中位表面偏差（MSD）和均方根差（RMS）进行了定量分析：结果：与 MR（840 ± 168.9 秒）和 SR（274.7 ± 100.7 秒）相比，AR 最省时，只需 51.4 ± 17.2 秒。与 MR 相比，AR 和 SR 的定性评分明显更高，更倾向于完美的 IOS-CBCT 配准（p = .001）。此外，AR 的定量性能明显优于 SR，表现为 MSD（0.04 ± 0.07 mm）和 RMS（0.19 ± 0.31 mm）较低。相比之下，MR 与 AR（MSD = 0.13 ± 0.20 mm；RMS = 0.32 ± 0.14 mm）和 SR（MSD = 0.11 ± 0.15 mm；RMS = 0.40 ± 0.30 mm）相比，差异明显更大：新颖的人工智能驱动方法提供了准确、省时和一致的多模态 IOS-CBCT 注册，包括软组织和硬组织。在术前种植规划工作流程中，这种方法是手动和半自动化配准方法的重要替代方案。

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.