Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis.

IF 1.7 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Imaging Science in Dentistry Pub Date : 2024-09-01 Epub Date: 2024-08-12 DOI:10.5624/isd.20240038
Mahmood Dashti, Sahar Ghaedsharaf, Shohreh Ghasemi, Niusha Zare, Elena-Florentina Constantin, Amir Fahimipour, Neda Tajbakhsh, Niloofar Ghadimi
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引用次数: 0

Abstract

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures.

Materials and methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command.

Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913).

Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

利用放射影像检测下颌骨骨折的深度学习和卷积神经网络算法评估:系统综述与荟萃分析。
目的:人工智能(AI)和深度学习算法在口腔医学中的应用,尤其是在处理放射影像方面的应用,已经明显增加。然而,关于这些算法检测下颌骨骨折准确性的详细信息仍然有限:这项荟萃分析是根据系统综述和荟萃分析首选报告项目(PRISMA)指南进行的。就人工智能算法在放射影像上检测下颌骨骨折的准确性生成了特定的关键词。然后,在 PubMed/Medline、Scopus、Embase 和 Web of Science 数据库中进行检索。采用诊断准确性研究质量评估2(QUADAS-2)工具评估所选研究的潜在偏倚。使用 STATA 17 版本(StataCorp,College Station,Texas,USA)和 metandi 命令对相关参数进行了汇总分析:结果:在审查的 49 项研究中,有 5 项符合纳入标准。所有入选研究都采用了卷积神经网络算法,尽管骨干结构各不相同,而且所有研究都对全景放射影像进行了评估。汇总分析的灵敏度为 0.971(95% 置信区间 [CI]:0.881-0.949),特异性为 0.813(95% CI:0.797-0.824),诊断几率比为 7.109(95% CI:5.27-8.913):本综述表明,深度学习算法具有在全景放射影像上检测下颌骨骨折的潜力。然而,由于可用数据集规模小、范围窄,其有效性目前受到限制。要验证这些工具在实际牙科环境中的准确性,对更大和更多样化的数据集进行进一步研究至关重要。
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
Imaging Science in Dentistry
Imaging Science in Dentistry DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.90
自引率
11.10%
发文量
42
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