A Mathematical Approach to Diagnosis of Oral Lesions - Diagnosis of Radiographic Images -

口腔病变诊断的数学方法 - 放射线图像诊断 -

• 批准号: 04671249
• 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 General Scientific Research (C)
• 财政年份: 1992
• 资助国家: 日本
• 起止时间: 1992 至 1993
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-04671249/
• 关键词: radiographic interpretation; mathematical approach; computer-aided; radiolucencies of the jaws; intra-osseous lesions

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 in a more utilizable and accessible form and can be quickly referred to concerning the case in question, it 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 9,376 of the 10,126 patients who were e … More xamined at the Department of Oral Radiology, Osaka Dental University during the 6 years between 1987 and 1992 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), i.e., the maxillary sinus, maxillary bones, mandibular bones, temporo-mandibular joint, salivary glands, soft tissues, and others. Radiographic images were classified into 7 types (18 patterns), i.e., normal, radiolucencies of the jaws, radiopacities of the jaws, partly radiolucencies and partly radiopacities of the Jaws, anomaly of skeletal bones, TMJ disorders, lesions of salivary glands, and others. Radiographical findings were most frequently radiolucencies of the jaws, which were observed in 64% of the patients, and inflammation accounted for 86% No changes, which were the next most frequent X-ray findings, were observed in the jaw bones in 14%. We derived a symptom-disease matrix of intra-osseous lesions from these data and calculated the posterior probability.This posterior probability is considered to assist interpretation of X-ray images for medical decision making. Less

只有全面评估大量信息，包括临床体征和症状、既往病史以及各种检查（包括放射学和组织病理学检查）的结果，才能对口腔病变做出准确诊断。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。但是，如果能够将积累的信息以更实用、更容易获取的方式进行分类，并能够针对该病例迅速进行参考，则可以在一定程度上缩小医生之间的经验差异，提高诊断的准确性。我们将患者的年龄、性别、病变部位、疾病和图像数据作为基础数据输入，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。10,126名患者中有9,376名患者的数据显示， ...更多信息 1987 ~ 1992年6年间在大坂齿科大学口腔放射科检查的10种疾病（68处病变），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），即，上颌窦、上颌骨、下颌骨、颞下颌关节、唾液腺、软组织等。X线图像分为7类（18种模式），即，正常、颌骨的射线可透性、颌骨的射线不透性、颌骨的部分射线可透性和部分射线不透性、骨骼骨的异常、TMJ病症、唾液腺病变等。X线检查结果最常见的是颌骨的射线可透性，在64%的患者中观察到，炎症占86%。在14%的患者中观察到颌骨无变化，这是第二常见的X线检查结果。我们从这些数据中推导出骨内病变的诊断-疾病矩阵，并计算后验概率，该后验概率被认为有助于解释X射线图像以进行医疗决策。少