Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.

IF 2.9 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Dento maxillo facial radiology Pub Date : 2024-09-01 DOI:10.1093/dmfr/twae031

Ziang Wu, Xinbo Yu, Yizhou Chen, Xiaojun Chen, Chun Xu

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引用次数: 0

Abstract

Objectives: To assess the performance of deep learning (DL) in the detection, classification, and segmentation of maxillary sinus diseases.

Methods: An electronic search was conducted by two reviewers on databases including PubMed, Scopus, Cochrane, and IEEE. All English papers published no later than February 7, 2024, were evaluated. Studies related to DL for diagnosing maxillary sinus diseases were also searched in journals manually.

Results: Fourteen of 1167 studies were eligible according to the inclusion criteria. All studies trained DL models based on radiographic images. Six studies applied to detection tasks, one focused on classification, two segmented lesions, and five studies made a combination of two types of DL models. The accuracy of the DL algorithms ranged from 75.7% to 99.7%, and the area under curves (AUC) varied between 0.7 and 0.997.

Conclusion: DL can accurately deal with the tasks of diagnosing maxillary sinus diseases. Students, residents, and dentists could be assisted by DL algorithms to diagnose and make rational decisions on implant treatment related to maxillary sinuses.

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深度学习在上颌窦疾病诊断中的应用：系统综述。

目的：评估深度学习（DL）在上颌窦疾病的检测、分类和分割中的性能：评估深度学习（DL）在上颌窦疾病的检测、分类和分割方面的性能：由两名审稿人对 PubMed、Scopus、Cochrane 和 IEEE 等数据库进行电子检索。对所有在 2024 年 2 月 7 日之前发表的英文论文进行了评估。此外，还在期刊上人工搜索了与诊断上颌窦疾病的 DL 相关的研究：根据纳入标准，1167 项研究中有 14 项符合条件。所有研究都基于放射影像对 DL 模型进行了训练。6项研究应用于检测任务，1项研究侧重于分类，2项研究对病变进行了分割，5项研究结合了2种类型的DL模型。DL算法的准确率在75.7%到99.7%之间，曲线下面积（AUC）在0.7到0.997之间：结论：DL 可以准确处理上颌窦疾病诊断任务。结论：DL 可以准确地完成上颌窦疾病的诊断任务，学生、住院医师和牙医可以利用 DL 算法进行诊断，并就与上颌窦相关的种植治疗做出合理的决策。

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

Dento maxillo facial radiology 医学-核医学

CiteScore

5.60

自引率

9.10%

发文量

审稿时长

4-8 weeks

期刊介绍： Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X

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