| Co-Investigator(Kenkyū-buntansha) |
INOUE Mashiro Osaka Dental Univ., Dentistry, Assistant professor, 歯学部, 講師 (50159993)
HAYASHI Yasuhisa Osaka Dental Univ., Dentistry, Assistant professor, 歯学部, 助手 (90164970)
ITAGAKI Keisuke Osaka Dental Univ., Dentistry, Assistant professor, 歯学部, 助手 (00151449)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1997: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1996: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Research Abstract |
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
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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
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