Abstract: The aims of the study are to design a Fuzzy Expert. System (FES) for ..... Planning in Periodontology, http://nnno.facebook.comltopic.php?uid=.
A Fuzzy Expert System Design for Diagnosis of Periodontal Dental Disease Novruz ALLAHVERDI
Tevfik AKCAN
Selcuk University
Selcuk University
Electronic and Computer Education Department
Electronic and Computer Education Department
Konya/TURKEY
Konya/TURKEY
Abstract: The aims of the study are to design a Fuzzy Expert System (FES) for diagnosis of periodontal dental disease by analyzing diagnosis, to define treatment methods and also to develop a computer program to help dentists for investigation, diagnosis and treatment of the disease. The designed FES is expected to help dentists by facilitating their job with the most correct diagnosis and treatment method. In addition, this system increases the intervention speed by reducing time loss. The FES to be used in diagnosing periodontal disease perceives the clinical and radiographic findings and determines the severity of disease as output. Expert system (ES) includes the severity of disease and other risk factors that cannot be fuzzified, and thus determines the type of disease and treatment method as output values. Contrubition of this study is to be the first work of fuzzy logic application in the area of periodontal dental disease. It has some
advantages
comparing with
traditional
diagnosis
and
treatment methods. Key
words:
Fuzzy
Expert
System,
Dental
Diagnosis,
Treatment, Computer Programme, Dentist
I. INTRODUCTION Periodontal disease is one of the most common disease in the world with different effects on different sections of society, and generally it is a chronic disease resulting in teeth loss by spreading in dental supporting tissues. The most commonly observed and investigated diseases of periodontium are plate gingivitis and chronic periodontitis that have inflammatory structure [1]. In case of not being treated the disease that starts as gingivitis will tum into chronic periodontitis that is characterized by attachment loss and pocket formation due to qualitative and quantitative development of pathogen bacteria in microbial dental plate and decrease in defense mechanism of patient [2]. As in all medical branches, to apply a successful treatment is only possible with correct diagnosis of the disease in the dentistry. The diagnose is implemented with mutual evaluation of clinical and radiographic examinations. In general, certain examination methods including anamnesis, inspection, palpation, and percussion are clinically used in all branches of dentistry [7]. In addition to these examinations, some other clinical examinations like plate index (PI), gingival index, clinical attachment level and MB index are used in periodontal field. However, these clinical examinations may never be enough for correct diagnosis. For this reason, findings obtained in clinical examinations should
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be evaluated together with radiographic examinations and systematic factors [6]. Results obtained from mutual evaluation of all these factors would be safer and more effective in recognition, diagnosis and treatment of disease. Technological tools and computer programs could also be used in this regard. This would help dentists in diagnosing and treating the disease and also following a correct path. In parallel with the rapid development in computer technology, the technology continues to develop in medicine, dentistry and other medical fields, as well [3]. Input variables of proposed FES are clinical and radiographic findings used in the diagnosis of disease whereas output variable is the severity of the disease. Later on, calculated severity of disease and other risk factors that cannot be fuzzified are included in the proposed ES, and thus the disease type and treatment method are produced as output values of whole proposed system. 11.
FUZZY EXPERT SYSTEM
FES is an expert system that evaluates the ambiguity and fuzzy information. Human experts store their knowledge in verbal terms in the real world. Therefore, it is quite natural to represent information with fuzzy rules and to use fuzzy inference methods. FES structure resembles to fuzzy logic controllers. As in fuzzy logic controllers, fuzzifier interface can be database and inference engine (decision making engine). Instead of clarification module, verbal approximation module can be used [4]. Inference process in FES is composed of four stages. These are fuzzify, inference, assembling and defuzzification. Fuzzify is the determination of membership functions for input and output variables subjected to real values to determine the correctness degree of each rule and assumption. Inference is the calculation of correct values for each rule and assumption and the application of these values to inference section of each [5]. Assembly is the integration of all fuzzy subsets assigned to each output variable in order to create one fuzzy subset for each output variable. Defuzzification is the process used to transform fuzzy output set into crisp numbers. Fuzzy subsets in FES are derived from logical relationships between membership values of membership functions. Proposed FES design is demonstrated in Fig. 1.
c) Probing Packet Depth (PPD): ranges between 0-8 mm. It is used to measure tissue loss. d) Mobility (MB): ranges between 0-3 mm. It is used to measure dental activity. e) Attachment Loss (AL): ranges between 0-9 units. It is used to measure the fiber loss connecting gingival to bone. As an example linguistic expressions and graphic of PPD input variable are presented in Table 1 and Fig. 2, respectively. Fig.
I. Fuzzy Expert System Schema
m. MATERTAL
AND METHOD
(2)
Changes occurring everyday in medical field are equally reflected upon the treatments of diseases. Considering the applications in medical area containing fuzzy logic and expert system, FES methods yielded more successful results in diagnosis and treatment than those made with normal methods [9].
(3)
(4)
After the decision of a fuzzy system design, the next step is to form "if-then" rules. These rules are generally constructed with the help of an expert [8]. Mamdani approach is used as output mechanism. The validity degree (a) is formulated for each rule by Mamdani max -min rule. The maximum validity degree of the triggered rules is calculated by the following equation: (1) Crisp values during the validity degree in defuzzification are obtained by "center of gravity" method [8]. There are lots of fuzzy studies in the different fields of medicine [11, 12, 13, 14, 15]. These works show that fuzzy control can be very useful in the field of medicine since many data in this field are fuzzy. Many studies have been performed regarding fuzzy logic in dental field. Among them, some examples include the use of FESs in the production of automatic polishing of cobalt chromium, automatic record of dental radiograph and the diagnosis of diseases. Clinical and laboratory methods are used for diagnosis and also determination of type of periodontal dental disease. Data obtained during examination are recorded and information is collected regarding the process and activity of disease. Tn the present study, a FES is developed for diagnosis of periodontal disease considering especially the clinical fmdings.
PPD (mm) LessDeep
MediumDeep
VeryDeep
Member
Index
Member
Index
Member
Index
I 1 0,67 0,33 ° ° ° ° °
° 1 2 3 4 5 6 7 8
° ° 0,33 0,67 I 0,67 0,33 ° °
° 1 2 3 4 5 6 7 8
° ° ° ° ° 0,33 0,67 I 1
° 1 2 3 4 5 6 7 8
Table
l. Probing Packet Depth Member Table
Exactness of periodontal disease is determined by these values. The certainity of periodontal disease includes two stages. These are: 1. Gingivitis 2. Periodontitis. PI'obuIg Faci{et Uel)th (mill)
In the designed FES, input variables are [16]; a) Gingival Index (Ol): ranges between 0-3 gi. It is used in the examination of gingival health. b) Alveolar Bone Loss (ABL): ranges between 0-9 mm. It is used to measure the bone loss, which is the supporting tissue of teeth.
Fig.
2. Probing Packet Depth Member Graphic
In addition, 6 sub-levels were formed. The aim was to obtain better results by increasing the sensitivity level of the disease.
of disease is produced as output according to input values of
(5)
Gingival Index (PPD),
(Ol),
Mobility (MOB), Probing Packet Depth
Attachment Loss (AL) and Alveolar Bone Loss
(ABL),
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