Program Expert System In Exsys Corvid Core For Mac
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Background: Attachment selection in removable partial dentures (RPD) design is considered one of the most challenging treatment modalities in dentistry. Any error that occur during attachment selection due to lack of proper knowledge, overwhelming number of attachments, mistreatment, multiple adjustments and repairs could result in adverse clinical consequences, and significant inconvenience to the patient as well as financial implication to both patient and provider. Attachment selection is indeed very challenging for several reasons. Firstly, the topic itself has not been widely researched and published in dental literature, and therefore the best attachment selection still remains an area prone to high error rates in decision-making. Secondly, the complexity of the topic and lack of proper knowledge that requires sound knowledge of attachment principle, which spans multiple dental displaces of endodontic, orthodontics, periodontics and prosthodontics. Furthermore, now there are an over whelming number of attachments available in the market due to high patient demand for cosmetic and aesthetic dental enhancements. It is therefore extremely difficult for dental practitioners to readily recall an extensive list of factors that determine an appropriate attachment for RPD design. This is more as for dental education students, especially for students, residents, and less experienced clinician who may not possess the adequate education, training and competencies. Although clinical experts in the area of RPD design and attachment experience and skills may be able to assist with knowledge and years of experience they may not always be around or readily available. To address this problem and gab in the education and training of dental students, residents and practitioners seeking continuing education, we have developed a clinical support and training system for RPD attachment design and implementation based on dental experts’ knowledge and literature evidence-based clinical and practice guidelines.
Methodology: The RPD attachment clinical decision support system was developed using Exsys Corvid Core software. The knowledge based of the system was setup using dental experts’ and literature evidence-based practice guidelines. In all the knowledge base was successfully loaded with more than 100 rules representing many different clinical scenarios for variable types of attachment selection in RPD. For any new input attachment case, based on the information entered by the user, the system comes up with an appropriate evidence-based recommendation and treatment plan. To ensure that the clinical decision support and training system was indeed fully capable of training and educating dental students and residents it was validated by nine expert prosthodontics using a survey style questionnaire on the various aspects of the setup and functionality of the system. The questionnaire results were statistically evaluated using Cronbach’s Alpha Coefficient Test.
Results: The Cronbach’s Alpha reliability coefficient was 0.893, which represent a good internal consistency and indicates an overall agreement among the prosthodontic experts as to the need and viability of the system for training dental students and residents in the area of RPD attachment design. Likewise, the results of the validation questionnaire showed that all prosthodontics agreed that the system contained all of the most relevant factors for attachment selection in RPD design ensuring its utility for training and education in a real-world practice.
Conclusion: The clinical decision support and training system for RPD attachment design was successfully developed using Exsys Corvid Core software. Expert prosthodontists concurred that the system can be effectively employed for training dental student, inexperienced dentists and residents to select an appropriate attachment for RPD. It can be used to complement traditional teaching methods even in the absence of patients as part of a dental degree curriculum.
Methodology: The RPD attachment clinical decision support system was developed using Exsys Corvid Core software. The knowledge based of the system was setup using dental experts’ and literature evidence-based practice guidelines. In all the knowledge base was successfully loaded with more than 100 rules representing many different clinical scenarios for variable types of attachment selection in RPD. For any new input attachment case, based on the information entered by the user, the system comes up with an appropriate evidence-based recommendation and treatment plan. To ensure that the clinical decision support and training system was indeed fully capable of training and educating dental students and residents it was validated by nine expert prosthodontics using a survey style questionnaire on the various aspects of the setup and functionality of the system. The questionnaire results were statistically evaluated using Cronbach’s Alpha Coefficient Test.
Results: The Cronbach’s Alpha reliability coefficient was 0.893, which represent a good internal consistency and indicates an overall agreement among the prosthodontic experts as to the need and viability of the system for training dental students and residents in the area of RPD attachment design. Likewise, the results of the validation questionnaire showed that all prosthodontics agreed that the system contained all of the most relevant factors for attachment selection in RPD design ensuring its utility for training and education in a real-world practice.
Conclusion: The clinical decision support and training system for RPD attachment design was successfully developed using Exsys Corvid Core software. Expert prosthodontists concurred that the system can be effectively employed for training dental student, inexperienced dentists and residents to select an appropriate attachment for RPD. It can be used to complement traditional teaching methods even in the absence of patients as part of a dental degree curriculum.
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