A Mathematical Approch to Image Diagnosis of Fibro-osseous lesions on Image Data Bank

图像数据库上纤维骨性病变图像诊断的数学方法

• 批准号: 08672173
• 负责人: KOSEKI Takakazu
• 金额: $ 1.28万
• 依托单位: Osaka Dental University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1996
• 资助国家: 日本
• 起止时间: 1996 至 1997
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08672173/
• 关键词: Radiographic interpretation; Mathematical approch; Computer-aided; Fibro-osseous lesions; Simple bone cyst; Idiopathic osteosclerosis; Periapical fibrous dysplasia; X線画像解析

Accurate diagnosis of oral lesions can be made only by corn-prehensive evaluation of a large quantity of information including in clinical signs and symptoms, and the past history as well as findings on various examinations including radiographical and histopathological examinations. Clinical experience over many years is needed for a physician to become able to select from a large volume of information and utilize only that which is useful However, if the accumulated information can be classified a more utilizable and accessible forms and can be quickly referred to concerning the case in question, ir would, to some extent, diminish the difference in experience among individual doctors and improve the accuracy of the diagnosis.We input the age, sex, site of the lesion, disease, and image data of patients as basic data, and calculated the probability of the correct diagnosis in computer-aided medical decision making based on these data. Data of 15,495 of the 20,104 patients who were exa … More mined at the Department of Oral Radiology, Osaka Dental University during the 10-years between 1987 and 1996 was classified and used as basic data.Ten disease categories (68 lesions), i.e., inflammation, odontogenic cysts, non-odontogenic cysts, odontogenic tumors, non-odontogenic tumors, malignant tumors, trauma, TMJ disorders, unclassifiable diseases, and others were established. The sites of the lesions were classified into 7 categories (32 sites). Radiographic images were classified into 6 types (18 patterns). Radiographic images were most frequently radiolucencies of the jaws, which were observed in 64% of the patients. The fibro-osseous lesions were classified into 9 diseases, i.e., Idieopathic osteo-sclerosis (73cases), Simple bone cyst (72cases), Periapical fibrous dysplasia (36cases), Ossifying fibroma (31cases), Fibrous dysplasia (30cases), Sclerotic cemental mass (17cases), Myxoma (13cases), and Others (13cases). Others were included Histiocytosis-X,Giant cell lesions, Benign cementoblastoma and et.al.We derived a symptom-disease matrix of intra-osseous lesions from these data and calculated the posterior probability and the statical decision tried to radiographic impression from radiographic images.The result of posterior probability was almost correct. But, the unusual radiographic images case were not correct. These posterior probability are considered to assist interpretation of radiographic images for medical decision making. Less

要对口腔病变作出准确的诊断，必须综合分析临床症状、体征、既往病史以及各种检查结果，包括放射学检查和组织病理学检查。医生需要多年的临床经验，才能从大量的信息中进行选择，并只利用有用的信息。然而，如果积累的信息能够被归类为更有用和可访问的形式，并能够针对所涉病例快速参考，则在一定程度上可以缩小医生之间的经验差异，提高诊断的准确性。我们输入患者的年龄、性别、病变部位、疾病和图像数据作为基本数据，并计算在基于这些数据的计算机辅助医疗决策中正确诊断的概率。20,104名退出…的患者中15,495名的数据以大阪牙科大学口腔放射科1987~1996年10年间采集的资料为基础资料，建立炎症、牙源性、非牙源性、牙源性肿瘤、非牙源性肿瘤、恶性肿瘤、创伤、颞下颌关节紊乱病、无法分类疾病等10大类68个病种。病变部位分为7类32个部位。X线影像分为6类(18种类型)。X线表现以颌骨透亮最为常见，%的患者可见颌骨透亮。其中特发性骨硬化症73例，单纯性骨囊肿72例，根尖周纤维异常增殖症36例，骨化性纤维瘤31例，纤维异常增殖症30例，硬化性骨质瘤17例，粘液瘤13例，其他13例。其他包括组织细胞增生症-X、巨细胞病变、良性牙骨质母细胞瘤等。我们根据这些数据推导出骨内病变的症状-疾病矩阵，并计算后验概率，静态决策试图从X线图像中获得影像印象，后验概率的结果几乎是正确的。但是，这一不寻常的放射影像案例并不正确。这些后验概率被认为有助于医学决策的放射学图像的解释。较少

项目成果

古跡 孝和: "歯科放射線の臨床診断:画像診断と病理概説" 山本 浩嗣、小林 馨, 194 (1997)

Takakazu Koseki：“牙科放射学的临床诊断：影像诊断和病理学概述”Hirotsugu Yamamoto、Kaoru Kobayashi，194（1997）