AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks.

IF 3.1 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Clinical Oral Investigations Pub Date : 2024-11-21 DOI:10.1007/s00784-024-06009-2

Marie Louise Slim, Reinhilde Jacobs, Renata Maíra de Souza Leal, Rocharles Cavalcante Fontenele

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引用次数: 0

Abstract

Objective: To develop and validate an artificial intelligence (AI)-driven tool for automated segmentation of the pulp cavity system of mandibular molars on cone-beam computed tomography (CBCT) images.

Materials and methods: After ethical approval, 66 CBCT scans were retrieved from a hospital database and divided into training (n = 26, 86 molars), validation (n = 7, 20 molars), and testing (n = 33, 60 molars) sets. After automated segmentation, an expert evaluated the quality of the AI-driven segmentations. The expert then refined any under- or over-segmentation to produce refined-AI (R-AI) segmentations. The AI and R-AI 3D models were compared to assess the accuracy. 30% of the testing sample was randomly selected to assess accuracy metrics and conduct time analysis.

Results: The AI-driven tool achieved high accuracy, with a Dice similarity coefficient (DSC) of 88% ± 7% for first molars and 90% ± 6% for second molars (p > .05). The 95% Hausdorff distance (HD) was lower for AI-driven segmentation (0.13 ± 0.07) compared to manual segmentation (0.21 ± 0.08) (p < .05). Regarding time efficiency, AI-driven (4.3 ± 2 s) and R-AI segmentation (139 ± 93 s) methods were the fastest, compared to manual segmentation (2349 ± 444 s) (p < .05).

Conclusion: The AI-driven segmentation proved to be accurate and time-efficient in segmenting the pulp cavity system in mandibular molars.

Clinical relevance: Automated segmentation of the pulp cavity system may result in a fast and accurate 3D model, facilitating minimal-invasive endodontics and leading to higher efficiency of the endodontic workflow, enabling anticipation of complications.

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利用卷积神经网络在 CBCT 图像上对下颌磨牙的牙髓腔系统进行人工智能驱动的分割。

目的开发并验证一种人工智能（AI）驱动的工具，用于在锥形束计算机断层扫描（CBCT）图像上自动分割下颌磨牙的牙髓腔系统：经伦理批准后，从医院数据库中获取了 66 张 CBCT 扫描图像，并将其分为训练集（n = 26，86 颗磨牙）、验证集（n = 7，20 颗磨牙）和测试集（n = 33，60 颗磨牙）。自动分割后，由专家对人工智能驱动的分割质量进行评估。然后，专家对任何不足或过度的分割进行细化，以生成细化人工智能（R-AI）分割。对人工智能和 R-AI 3D 模型进行比较，以评估准确性。随机抽取 30% 的测试样本来评估准确度指标并进行时间分析：人工智能驱动的工具达到了很高的准确度，第一磨牙的骰子相似系数（DSC）为 88% ± 7%，第二磨牙为 90% ± 6%（p > .05）。人工智能驱动的95%豪斯多夫距离（HD）（0.13±0.07）低于手动分割（0.21±0.08）（p 结论：人工智能驱动的分割准确率为88%±7%，第二磨牙为90%±6%（p > .05）：在下颌磨牙牙髓腔系统的分割中，人工智能驱动的分割被证明是准确和省时的：牙髓腔系统的自动分段可产生快速、准确的三维模型，促进微创牙髓治疗，提高牙髓治疗工作流程的效率，预测并发症的发生。

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 Investigations 医学-牙科与口腔外科

CiteScore

6.30

自引率

5.90%

发文量

484

审稿时长

3 months

期刊介绍： The journal Clinical Oral Investigations is a multidisciplinary, international forum for publication of research from all fields of oral medicine. The journal publishes original scientific articles and invited reviews which provide up-to-date results of basic and clinical studies in oral and maxillofacial science and medicine. The aim is to clarify the relevance of new results to modern practice, for an international readership. Coverage includes maxillofacial and oral surgery, prosthetics and restorative dentistry, operative dentistry, endodontics, periodontology, orthodontics, dental materials science, clinical trials, epidemiology, pedodontics, oral implant, preventive dentistiry, oral pathology, oral basic sciences and more.