Fuzzy Logic Based Expert System for the Treatment of Mobile Tooth

Mago, Vijay; Mago, Anjali; Sharma, Poonam; Mago, Jagmohan

doi:10.1007/978-1-4419-7046-6_62

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…The fuzzy sets are equidistant and the parameters for the membership functions are defined intuitively by experts. This approach of relying upon expert opinion to define the fuzzy sets has been suggested in [ 47 ] and used in our previous research works [ 48 , 49 ]. Figure 3 shows the membership functions (triangular and trapezoidal) for output variable Mn , given the input variable Cellulitis/Infective Focus .…”

Section: Methodsmentioning

confidence: 99%

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

Mago

Mehta

Woolrych

et al. 2012

BMC Med Inform Decis Mak

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BackgroundMeningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition.MethodsFuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team.ResultsThe paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians’ decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results with sensitivity of 83.3% and specificity of 80%.ConclusionsThis work suggests that the application and development of a knowledge based system, using the formalization of FCMs for understanding the symptoms and causes of meningitis in children and infants, can provide a reliable front-end decision-making tool to better assist physicians.

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Section: Methodsmentioning

confidence: 99%

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

Mago

Mehta

Woolrych

et al. 2012

BMC Med Inform Decis Mak

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show abstract

“…The goal of fuzzy logic is to mimic human reasoning abilities that function with ambiguous concepts. To aid dental professionals in making decisions, Mago et al created a fuzzy logic based expert system that allows imprecise values of dental signsymptoms linked with mobile teeth [31]. Ambara and co-workers devised a fuzzy logic-based expert system that is both time and cost effective, and can be conducted without the use of any equipment reducing the patient's stress [32].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

2021

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Artificial Intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the healthcare industry. In the domains of dentistry and medicine, artificial intelligence has yet to come a long way. As a result, dentists must be aware of the potential implications for a profitable clinical practise in the future. In this paper, we present the current applications of AI in dentistry. The different types of AI techniques are introduced and summarized. The state-of-the-art literature is studied analysed. A comparative analysis on the different AI techniques in dentistry is presented. Further, the research challenges in the field of dentistry and future directions are also provided.

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“…The aim of fuzzy logic is to mimic human reasoning abilities that function with ambiguous words. To aid dental practitioners in making decisions, Mago et al created a fuzzy logic based expert system that recognises imprecise values of dental sign-symptoms associated with mobile teeth [12] . Oral and dental diseases affect people of all ages, but often people neglect them.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Artificial intelligence in the field of dentistry

Handa¹

2021

Int. J. Comput. Artif. Intell.

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Humans have resurrected intelligence in order to make better decisions and relieve themselves of a massive workload. Artificial intelligence, or AI, is a computer program's ability to learn complex patterns by training it to perform acts that imitate human learning and problem-solving abilities. While artificial intelligence is still a young field, it has made significant progress in the fields of medicine and dentistry. As a result, dentists must be mindful of the possible consequences for a profitable clinical practice in the future. Any AI application in dentistry should show tangible value by enhancing access to and quality of care, increasing service efficiency and safety, inspiring and enabling patients, promoting medical research, or increasing sustainability, for example. Individual privacy, privileges, and autonomy must be prioritised; a transition from centralised to distributed learning may help with this while also enhancing scalability and robustness. Dental education would need to go hand-in-hand with the implementation of clinical AI technologies, promoting digital literacy among the potential dental workforce. The culmination of artificial intelligence along with digitization has seen a new era in the field of dentistry and its future aspects appear extremely promising.

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Fuzzy Logic Based Expert System for the Treatment of Mobile Tooth

Cited by 14 publications

References 13 publications

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

Artificial intelligence in the field of dentistry

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