Automatic deep learning detection of overhanging restorations in bitewing radiographs.

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/twae036
Guldane Magat, Ali Altındag, Fatma Pertek Hatipoglu, Omer Hatipoglu, İbrahim Sevki Bayrakdar, Ozer Celik, Kaan Orhan
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Abstract

Objectives: This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.

Methods: A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.

Results: The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.

Conclusions: The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

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深度学习自动检测咬翼X光片中的悬垂修复体。
研究目的本研究旨在评估深度卷积神经网络(CNN)算法在检测和分割咬翼X光片中的悬雍垂修复体方面的有效性:共使用了 1160 张匿名咬合X光片来改进人工智能系统(Artificial Intelligence (AI) system)对悬吊修复体的检测和分割。然后将数据分为三组:80%用于训练(930 张图像,2399 个标签),10%用于验证(115 张图像,273 个标签),10%用于测试(115 张图像,306 个标签)。对名为 "你只看一次"(YOLOv5)的 CNN 模型进行了训练,以检测咬翼X光片中的悬垂修复体。利用剩余的 115 张 X 光片评估了所提出的 CNN 模型的功效,并计算了准确度、灵敏度、精确度、F1 分数和接收器工作特征曲线下面积(AUC):该模型的精确度为 90.9%,灵敏度为 85.3%,F1 分数为 88.0%。此外,该模型在接收者操作特征曲线(ROC)上的AUC达到了0.859。在交集大于联合(IoU)阈值为 0.5 时,平均精确度(mAP)明显较高,达到 0.87:研究结果表明,深度 CNN 算法在检测和诊断咬合X光片中的悬雍垂牙修复体方面非常有效。高精确度、高灵敏度、高 F1 得分以及显著的 AUC 和 mAP 值,凸显了这些先进的深度学习技术在革新牙科诊断程序方面的潜力。
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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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