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Shah, A.B. (2014) Decision Support and Training System for Management of Endodontically Treated Teeth. Doctoral Dissertation, Rutgers University, New Brunswick.

has been cited by the following article:

• TITLE: Expert System for the Diagnosis and Prognosis of Common Dental Diseases Using Bayes Network

  AUTHORS: Grace Tam-Nurseman, Philip Achimugu, Oluwatolani Achimugu, Hilary Kelechi Anabi, Sseggujja Husssein

  KEYWORDS: Expert System, Dental Diagnosis, Dental Diseases and Human Expert

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.14 No.11, November 30, 2021

  ABSTRACT: Expert systems are being utilized increasingly in medical fields for the purposes of assisting diagnosis and treatment planning. Existing systems used few symptoms for dental diagnosis. In Dentistry, few symptoms are not enough for diagnosis. In this research, a conditional probability model (Bayes rule) was developed with increased number of symptoms associated with a disease for diagnosis. A test set of recurrent cases was then used to test the diagnostic capacity of the system. The generated diagnosis matched that of the human experts. The system was also tested for its capacity to handle uncommon dental diseases and the system portrayed useful potential.