| Co-Investigator(Kenkyū-buntansha) |
SHINYA Akiyuki Showa University, School of Dentistry, Associate Professor, 歯学部, 助教授 (10119208)
FUNATO Masahiko Showa University, School of Dentistry, Lecturer, 歯学部, 講師 (10146897)
SUGANUMA Takeshi Showa University, School of Dentistry, Lecturer, 歯学部, 講師 (10196694)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2006: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2005: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Research Abstract |
On a database construction of patient with temporomandibular (TM) disorder, we made a questionnaire and a protocol concluding of a chief complaint, present observations and three biggest symptoms. We established items to fill out by TM joint sounds, pain, trismus, X-rays and MRI views as image information, occlusal states, anterior guidance, occlusal interferences, soft tissue views, mouth open-close paths, mouth opening distance, psychophysiological anxiety and depression scores, diagnostic categories of TM disorder, therapy and treatment evaluation. We made formats to input a database that link to this protocol and we input patient datas obtained from excellent treatment results into a personal computer and were able to build databases. As a result, we showed excellent rates of 73-83% about treatment evaluation, and education for patients and physical therapy were common, and, as for the thing that treatment was effective, sprint therapy and pharmacotherapy were found in type I and type II, pharmacotherapy and surgical therapy in type IIIb, sprint therapy in type IV. When an intelligent knowledge-based system treats knowledge about TM disorder, a diagnosis of TM disorder is thought to be useful and can plan problem solving by even what kind of case is common and uses a usable reasoning mechanism, and treating the knowledge base that accumulated as a database at this time. A knowledge base constitutes TM joint sound unit, pain unit, mouth opening distance unit, imaging information unit and other symptom unit as a prototype system and we decide a rule which of these units it applies to and produce diagnosis unit. And it is necessary to decide a rule to determine therapy from obtained abnormal findings. It was thought that an intelligent knowledge-based system was built by an engineer having theoretical basics about artificial intelligence and basics of methodology about knowledge engineering afterwards.
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