{"title":"重塑中耳炎诊断:嵌套 U-Net 细分与图论启发特征集的融合","authors":"Sami Azam , Md Awlad Hossain Rony , Mohaimenul Azam Khan Raiaan , Kaniz Fatema , Asif Karim , Mirjam Jonkman , Jemima Beissbarth , Amanda Leach , Friso De Boer","doi":"10.1016/j.array.2024.100362","DOIUrl":null,"url":null,"abstract":"<div><p>Otitis media (OM) is a common infection or inflammation of the middle ear causing conductive hearing loss that primarily affects children and may delay speech, language, and cognitive development. OM can manifest itself in different forms, and can be diagnosed using (video) otoscopy (visualizing the tympanic membrane) or (video) pneumatic otoscopy and tympanometry. Accurate diagnosis of OM is challenging due to subtle differences in otoscopic features. This research aims to develop an automated computer-aided design (CAD) system to assist clinicians in diagnosing OM using otoscopy images. The ground truths, generated manually and validated by otolaryngologists, are utilized to train the proposed nested U-Net++ model. Ten clinically relevant gray level co-occurrence matrix (GLCM) and morphological features were extracted from the segmented Region of Interest (ROI) and validated for OM classification based on a statistical significance test. These features serve as input for a Graph Neural Network (GNN) model, the base model in our research. An optimized GNN model is proposed after ablation study of the base model. Three datasets, one private dataset, and two public ones have been used, where the private dataset is utilized for both training and testing, and the public datasets are used to test the robustness of the proposed GNN model only. The proposed GNN model obtained the highest accuracy in diagnosing OM: 99.38 %, 93.51 %, and 91.38 % for the private dataset, public dataset1, and public dataset2, respectively. The proposed methodology and results of this research might enhance clinicians' effectiveness in diagnosing OM.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"23 ","pages":"Article 100362"},"PeriodicalIF":2.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000286/pdfft?md5=206b3948d729d466a159c76421c4e068&pid=1-s2.0-S2590005624000286-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Reimagining otitis media diagnosis: A fusion of nested U-Net segmentation with graph theory-inspired feature set\",\"authors\":\"Sami Azam , Md Awlad Hossain Rony , Mohaimenul Azam Khan Raiaan , Kaniz Fatema , Asif Karim , Mirjam Jonkman , Jemima Beissbarth , Amanda Leach , Friso De Boer\",\"doi\":\"10.1016/j.array.2024.100362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Otitis media (OM) is a common infection or inflammation of the middle ear causing conductive hearing loss that primarily affects children and may delay speech, language, and cognitive development. OM can manifest itself in different forms, and can be diagnosed using (video) otoscopy (visualizing the tympanic membrane) or (video) pneumatic otoscopy and tympanometry. Accurate diagnosis of OM is challenging due to subtle differences in otoscopic features. This research aims to develop an automated computer-aided design (CAD) system to assist clinicians in diagnosing OM using otoscopy images. The ground truths, generated manually and validated by otolaryngologists, are utilized to train the proposed nested U-Net++ model. Ten clinically relevant gray level co-occurrence matrix (GLCM) and morphological features were extracted from the segmented Region of Interest (ROI) and validated for OM classification based on a statistical significance test. These features serve as input for a Graph Neural Network (GNN) model, the base model in our research. An optimized GNN model is proposed after ablation study of the base model. Three datasets, one private dataset, and two public ones have been used, where the private dataset is utilized for both training and testing, and the public datasets are used to test the robustness of the proposed GNN model only. The proposed GNN model obtained the highest accuracy in diagnosing OM: 99.38 %, 93.51 %, and 91.38 % for the private dataset, public dataset1, and public dataset2, respectively. 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引用次数: 0
摘要
中耳炎(OM)是一种常见的中耳感染或炎症,会导致传导性听力损失,主要影响儿童,并可能延迟言语、语言和认知能力的发展。中耳炎的表现形式多种多样,可通过(视频)耳内窥镜检查(观察鼓膜)或(视频)气动耳内窥镜检查和鼓室测量来诊断。由于耳镜特征的细微差别,准确诊断鼓室炎具有挑战性。本研究旨在开发一种自动计算机辅助设计(CAD)系统,以协助临床医生使用耳镜图像诊断耳鸣。利用人工生成并经耳鼻喉科医生验证的基本事实来训练所提出的嵌套 U-Net++ 模型。从分割的感兴趣区(ROI)中提取了十个与临床相关的灰度共现矩阵(GLCM)和形态学特征,并根据统计显著性测试对 OM 分类进行了验证。这些特征作为图神经网络(GNN)模型的输入,是我们研究的基础模型。在对基础模型进行消融研究后,我们提出了一个优化的 GNN 模型。我们使用了三个数据集,一个私有数据集和两个公共数据集,其中私有数据集用于训练和测试,公共数据集仅用于测试所提出的 GNN 模型的鲁棒性。在私人数据集、公共数据集 1 和公共数据集 2 中,所提出的 GNN 模型诊断 OM 的准确率最高:分别为 99.38 %、93.51 % 和 91.38 %。本研究提出的方法和结果可提高临床医生诊断 OM 的效率。
Reimagining otitis media diagnosis: A fusion of nested U-Net segmentation with graph theory-inspired feature set
Otitis media (OM) is a common infection or inflammation of the middle ear causing conductive hearing loss that primarily affects children and may delay speech, language, and cognitive development. OM can manifest itself in different forms, and can be diagnosed using (video) otoscopy (visualizing the tympanic membrane) or (video) pneumatic otoscopy and tympanometry. Accurate diagnosis of OM is challenging due to subtle differences in otoscopic features. This research aims to develop an automated computer-aided design (CAD) system to assist clinicians in diagnosing OM using otoscopy images. The ground truths, generated manually and validated by otolaryngologists, are utilized to train the proposed nested U-Net++ model. Ten clinically relevant gray level co-occurrence matrix (GLCM) and morphological features were extracted from the segmented Region of Interest (ROI) and validated for OM classification based on a statistical significance test. These features serve as input for a Graph Neural Network (GNN) model, the base model in our research. An optimized GNN model is proposed after ablation study of the base model. Three datasets, one private dataset, and two public ones have been used, where the private dataset is utilized for both training and testing, and the public datasets are used to test the robustness of the proposed GNN model only. The proposed GNN model obtained the highest accuracy in diagnosing OM: 99.38 %, 93.51 %, and 91.38 % for the private dataset, public dataset1, and public dataset2, respectively. The proposed methodology and results of this research might enhance clinicians' effectiveness in diagnosing OM.