利用颞下颌关节全景投影图像诊断颌面部畸形患者髁状突骨关节炎的深度学习分类性能。

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-07-11 DOI:10.1007/s11282-024-00768-0
Yukiko Iwase, Tomoya Sugiki, Yoshitaka Kise, Masako Nishiyama, Michihito Nozawa, Motoki Fukuda, Yoshiko Ariji, Eiichiro Ariji
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引用次数: 0

摘要

研究目的本研究旨在利用颞下颌关节(TMJ)全景投影图像,评估深度学习(DL)模型在颌面部畸形患者髁状突骨关节炎(OA)诊断中的一致性和性能:为了验证使用 252 个颞下颌关节紊乱和颌面部畸形患者有无髁突 OA 的颞下颌关节创建的 DL 模型的一致性和性能,共测试了 68 个颌面部畸形患者有无髁突 OA 的颞下颌关节;这些模型被用于在传统全景(Con-Pa)图像以及开放式(Open-TMJ)和闭合式(Closed-TMJ)口腔颞下颌关节投影图像上诊断 OA。GoogLeNet 和 VGG-16 网络用于创建 DL 模型。为了进行比较,两名牙科住院医师与结果进行了比较:在开放颞下颌投影图像上,DL 模型显示出中等到非常好的一致性,而住院医师在所有图像上都显示出一般的一致性。两个 DL 模型在 Con-Pa(GoogLeNet 为 0.84,VGG-16 为 0.75)和 Open-TMJ 图像(两个模型均为 0.89)上的曲线下面积(AUCs)都明显高于住院医师的 AUCs(p 结论:本研究创建的 DL 模型在所有图像上都具有很好的一致性:本研究创建的 DL 模型可帮助住院医师在诊断髁突 OA 时解释 Con-Pa 和 Open-TMJ 图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

Objective: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images.

Methods: A total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models.

Results: On Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models).

Conclusions: The DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA.

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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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