Application of fuzzy reasoning in an expert system for ultrasonography.

Tanaka, Toshinobu; Miwa, Kenji; Kanda, Shigenobu

doi:10.1038/sj.dmfr.4600225

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…20 Attempts have been made to incorporate "fuzzy logic" 35 into computer-based expert systems to allow for the normal imprecision or continuous interaction of patientclinician encounters. 36 "Fuzzy" concepts, based on the means and standard deviations of responses from clinical experts, have been used to select treatment for specific malocclusions 37,38 and for designing orthodontic headgear. 39 The orthodontists agreed almost unanimously (96 percent) on the design for the headgear, which simplified greatly the mathematical incorporation of the fuzzy functions into the decision-support system.…”

Section: Discussionmentioning

confidence: 99%

A Measure of Agreement Between Clinicians and a Computer‐Based Decision Support System for Planning Dental Treatment

Kawahata

MacEntee

2002

Journal of Dental Education

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This study was conducted to estimate agreement and explain differences between treatment decisions and associated fees recommended by dentists and by a computer-based decision-support program (TxDENT 2.0 © ). The treatment fees associated with forty-eight clinical records of patients attending a dental school clinic provided a measure of correlation and agreement between treatments recommended by TxDENT and by clinical instructors with students. The average difference between the two methods of forecasting fees was $466, and a regression line (y=0.43x+407) with an r-value of 0.54 indicated the strength of the relationship. The differences between methods increased as the cost of treatment increased, due largely to disagreements about the need to restore or replace weak or missing teeth. There is reasonable agreement between TxDENT and the collaborative treatment plans of clinical instructors with their students, which suggests that this computer-based decision-support system for screening patients in a standardized way could be a helpful predictor of treatment provided in a dental school clinic.

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Section: Discussionmentioning

confidence: 99%

A Measure of Agreement Between Clinicians and a Computer‐Based Decision Support System for Planning Dental Treatment

Kawahata

MacEntee

2002

Journal of Dental Education

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“…The "Others" class consisted of studies concerning temporomandibular disorders (TMD), oral malodor, and fraud in dentistry. Regarding the number of published studies, eight studies reported on "Conservative dentistry" [17][18][19][20][21][22][23][24], seven on "Periodontitis" [25][26][27][28][29][30][31], six on "Prosthodontics" [32][33][34][35][36][37], eight on "Orthodontics" [38][39][40][41][42][43][44][45], thirteen on "Oral and maxillofacial surgery" [46][47][48][49][50][51][52][53][54][55][56][57][58], and ten on "Forensic dentistry" [59][60][61][62][63][64][65]…”

Section: Study Selectionmentioning

confidence: 99%

Application of artificial intelligence in the dental field: A literature review

et al. 2022

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Recently, advanced in information science and technology have led to an explosive increase in data in various fields. Therefore, the importance of comprehensively processing enormous amounts of data, often called big data, has been highlighted, and hence, artificial intelligence (AI) has been widely used [1][2][3]. AI is a conceptual term denoting a series of basic technologies that enable digital systems or computers to perform functions involving human-like intelligence. The concept of AI was first defined by McCarthy in 1956[4]. In various industrial fields, many studies have considered the social implementations of big data analysis, robot control, voice or image recognition facilities, and automated driving using AI [5][6][7]. Studies have also been conducted to investigate the application of AI in the medical field. In particular, studies on expert systems such as MY-CIN, which is a recommended antibacterial drug selection system for diagnosing infectious diseases, and INTERNIST-1, which is a general medical diagnosis support system, were reported in 1974 [8,9]. Several decades later, Hinton et al. (2006) developed deep learning and convolutional neural networks (CNNs), which were presented at the ImageNet Large-Scale Visual Recognition Challenge in 2012 [10]. Currently, many studies related to the application of CNNs to classify brain tumors or diagnose cancer from skin images are being conducted worldwide [11][12][13]. Materials and MethodsIn this study, we investigated the purpose of AI application in the dental field and the individual problems that it aimed to resolve.

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“…They reported that their system produced more acceptable treatment plans than those used by general dental practitioners. Similarly, Tanaka et al (1997) [36] applied fuzzy reasoning to their computer-assisted diagnostic system for ultrasonography for the purpose of providing a diagnostic aid for unskilled clinicians. (head gear)…”

Section: Applications Of Ai Techniquesmentioning

confidence: 99%

“…Expert Systems: An expert system consists of a knowledge base, database and an inference engine for interpreting the database using the knowledge supplied in the knowledge base. [11] has been applied to dental and medical sciences n3(Sims- Williams et al, 1987) in order to construct systems that can infer precise recommendations for solving problems that have uncertain properties [32][33][34][35][36] . Brown et al (1991) [37] applied fuzzy logic to solve orthodontic problems in an expert system, designed to provide advice for treatment planning of Class II division 1 malocclusions.…”

Section: Applications Of Ai Techniquesmentioning

confidence: 99%

The Artificial Intelligence Approach for Diagnosis, Treatment and Modelling in Orthodontic

Bahaa¹,

Noor²,

Yousif³

2011

Principles in Contemporary Orthodontics

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Application of fuzzy reasoning in an expert system for ultrasonography.

Abstract: Application of fuzzy reasoning in an expert system for ultrasonography improves the diagnostic performance of inexperienced clinicians.

Cited by 6 publications

References 8 publications

A Measure of Agreement Between Clinicians and a Computer‐Based Decision Support System for Planning Dental Treatment

A Measure of Agreement Between Clinicians and a Computer‐Based Decision Support System for Planning Dental Treatment

Application of artificial intelligence in the dental field: A literature review

The Artificial Intelligence Approach for Diagnosis, Treatment and Modelling in Orthodontic

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