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

口腔病变的准确诊断需要综合大量的临床症状、体征、既往病史、影像学和组织病理学等检查资料。医生需要多年的临床经验，才能从大量信息中进行选择，并只利用有用的信息。然而，如果积累的信息可以分类为更有用和更容易获得的形式，并可以快速参考有关病例，那么在某种程度上，减少医生个体经验的差异，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础数据，并基于这些数据计算计算机辅助医疗决策中正确诊断的概率。20，104例患者中有15，495例患者的数据为 ...更多信息 1987年至1996年10年间在大坂齿科大学口腔放射学系采集的10种疾病类别（68种病变）被分类并用作基本数据。炎症、牙源性囊肿、非牙源性囊肿、牙源性肿瘤、非牙源性肿瘤、恶性肿瘤、创伤、TMJ病症、无法分类的疾病等。病变部位分为7类（32个部位）。影像学表现分为6类18种类型。放射学图像最常见的是钳口的射线可透性，在64%的患者中观察到。纤维骨性病变分为9种疾病，特发性骨硬化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，硬化性牙骨质肿块17例，粘液瘤13例，其他13例。根据这些数据建立了骨内病变的后验概率矩阵，并对X-组织细胞病、巨细胞病、良性成牙骨质细胞瘤等进行了统计学处理，后验概率结果基本正确。但是，不寻常的放射影像病例是不正确的。这些后验概率被认为是为了帮助医学决策的射线照相图像的解释。少

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