Enhanced multistage deep learning for diagnosing anterior disc displacement in the temporomandibular joint using MRI.

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Dento maxillo facial radiology Pub Date : 2024-10-01 DOI:10.1093/dmfr/twae033
Chang-Ki Min, Won Jung, Subin Joo
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Abstract

Objectives: This study aimed to propose a new method for the automatic diagnosis of anterior disc displacement of the temporomandibular joint (TMJ) using MRI and deep learning. By using a multistage approach, the factors affecting the final result can be easily identified and improved.

Methods: This study introduces a multistage automatic diagnostic technique using deep learning. This process involves segmenting the target from MR images, extracting distance parameters, and classifying the diagnosis into 3 classes. MRI exams of 368 TMJs from 204 patients were evaluated for anterior disc displacement. In the first stage, 5 algorithms were used for the semantic segmentation of the disc and condyle. In the second stage, 54 distance parameters were extracted from the segments. In the third stage, a rule-based decision model was developed to link the parameters with the expert diagnosis results.

Results: In the first stage, DeepLabV3+ showed the best result (95% Hausdorff distance, Dice coefficient, and sensitivity of 6.47 ± 7.22, 0.84 ± 0.07, and 0.84 ± 0.09, respectively). This study used the original MRI exams as input without preprocessing and showed high segmentation performance compared with that of previous studies. In the third stage, the combination of SegNet and a random forest model yielded an accuracy of 0.89 ± 0.06.

Conclusions: An algorithm was developed to automatically diagnose TMJ-anterior disc displacement using MRI. Through a multistage approach, this algorithm facilitated the improvement of results and demonstrated high accuracy from more complex inputs. Furthermore, existing radiological knowledge was applied and validated.

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利用磁共振成像诊断颞下颌关节前椎间盘移位的增强型多级深度学习。
研究目的本研究旨在提出一种利用磁共振成像(MRI)和深度学习自动诊断颞下颌关节(TMJ)前椎间盘移位的新方法。通过采用多阶段方法,可以轻松识别并改进影响最终结果的因素:本研究介绍了一种使用深度学习的多阶段自动诊断技术。该过程包括从磁共振图像中分割目标、提取距离参数并将诊断结果分为三类。对 204 名患者的 368 个颞下颌关节的 MRI 检查进行了椎间盘前移位评估。在第一阶段,使用五种算法对椎间盘和髁状突进行语义分割。第二阶段,从分段中提取 54 个距离参数。在第三阶段,开发了一个基于规则的决策模型,将参数与专家诊断结果联系起来:在第一阶段,DeepLabV3+ 的结果最好(95% Hausdorff 距离、Dice 系数和灵敏度分别为 6.47 ± 7.22、0.84 ± 0.07 和 0.84 ± 0.09)。该研究使用原始核磁共振成像检查作为输入,未进行预处理,与之前的研究相比,显示出较高的分割性能。在第三阶段,结合 SegNet 和随机森林模型得出的准确率为 0.89 ± 0.06:利用核磁共振成像开发了一种自动诊断颞下颌关节前盘移位的算法。通过多阶段方法,该算法促进了结果的改进,并从更复杂的输入中显示出较高的准确性。此外,现有的放射学知识也得到了应用和验证。
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来源期刊
CiteScore
5.60
自引率
9.10%
发文量
65
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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