Automated Detection of Maxillary Sinus Opacifications Compatible with Sinusitis from CT Images.

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Dento maxillo facial radiology Pub Date : 2024-08-06 DOI:10.1093/dmfr/twae042
Kyung Won Kwon, Jihun Kim, Dongwoo Kang
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Abstract

Background: Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number of maxillary sinus opacifications are diagnosed as sinusitis, often overlooking the precise differentiation between cystic formations and inflammatory sinusitis, resulting in inappropriate clinical treatment. This study aims to improve diagnostic accuracy by investigating the feasibility of differentiating maxillary sinusitis, retention cysts, and normal sinuses.

Methods: We developed a deep learning-based automatic detection model to diagnose maxillary sinusitis using ostiomeatal unit computed tomography images. Of the 1080 randomly selected coronal-view CT images, including 2158 maxillary sinuses, datasets of maxillary sinus lesions comprised 1138 normal sinuses, 366 cysts, and 654 sinusitis based on radiographic findings, and were divided into training (n = 648 CT images), validation (n = 216), and test (n = 216) sets. We utilized a You Only Look Once based model for object detection, enhanced by the transfer learning method. To address the insufficiency of training data, various data augmentation techniques were adopted, thereby improving the model's robustness.

Results: The trained You Only Look Once version 8 nano (YOLOv8n) model achieved an overall precision of 97.1%, with the following class precisions on the test set: normal = 96.9%, cyst = 95.2%, and sinusitis = 99.2%. With an average F1 score of 95.4%, the F1 score was the highest for normal, then sinusitis, and finally, cysts. Upon evaluating a performance on difficulty level, the precision decreased to 92.4% on challenging test dataset.

Conclusions: The developed model is feasible for assisting clinicians in screening maxillary sinusitis lesions.

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从 CT 图像自动检测与鼻窦炎相容的上颌窦不全
背景:鼻窦炎是一种常见的临床病症,给医疗系统造成了相当大的负担。大量上颌窦不张被诊断为鼻窦炎,但往往忽略了囊性形成和炎性鼻窦炎之间的精确区分,从而导致不恰当的临床治疗。本研究旨在通过研究区分上颌窦炎、潴留囊肿和正常鼻窦的可行性,提高诊断的准确性:方法:我们开发了一种基于深度学习的自动检测模型,利用骨窗单元计算机断层扫描图像诊断上颌窦炎。在随机选取的 1080 张冠状视角 CT 图像(包括 2158 个上颌窦)中,上颌窦病变数据集包括 1138 个正常上颌窦、366 个囊肿和 654 个基于放射学检查结果的上颌窦炎,并分为训练集(n = 648 CT 图像)、验证集(n = 216)和测试集(n = 216)。我们采用了基于 "只看一次 "的对象检测模型,并通过迁移学习方法进行了增强。为了解决训练数据不足的问题,我们采用了各种数据增强技术,从而提高了模型的鲁棒性:结果:训练后的 "只看一次 "8 纳米版(YOLOv8n)模型的总体精确度达到了 97.1%,测试集上的分类精确度如下:正常 = 96.9%、囊肿 = 95.2%、鼻窦炎 = 99.2%。平均 F1 得分为 95.4%,正常人的 F1 得分最高,其次是鼻窦炎,最后是囊肿。在对难度进行性能评估时,具有挑战性的测试数据集的精确度下降到 92.4%:结论:所开发的模型可以帮助临床医生筛查上颌窦炎病变。
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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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