Volume 2, Issue 2 (8-2013)                   3dj 2013, 2(2): 1-8 | Back to browse issues page

XML Print

1- Islamic Azad University, Noor Branch, Noor, Iran.
2- Oral and Maxillofacial Pathology, Dental School, Ardabil University of Medical Sciences, Ardabil, Iran
3- Guilan University of Medical Sciences, Guilan, Iran
Abstract:   (5965 Views)
Introduction: Diagnosis, prediction and control of oral lesions is usually done classically based on clinical signs and histopathologic features. Due to lack of timely diagnosis in all conventional methods or differential diagnosis, biopsy of patient is needed. Therefore, the patient might be irritated. So, an intelligent method for quick and accurate diagnosis would be crucial. Intelligent systems approach has been successful in prediction and diagnosis of factors. Intelligent instruments significantly contributed to the diagnosis of different disease, such as timely diagnosis of breast cancer, heart failures and so on. In this research, three of the most common and relatively dangerous oral diseases (lichen planus, leukoplakia and squamous cell carcinoma) have been studied using intelligent systems based on artificial neural networks (ANN). Materials and Methods: In this study, the population of the study constituted one hundred and fifty, fifty patients are considered in each disease. The four features for learning intelligent systems given to it as an input. Results: The output of this system includes charts and tables to determine the optimal prediction of machine. One of the figures represents the descent of error that is convergent to the global optimum. The extreme prediction of machine would be achievable with the least amount of attention. So, the credibility and reliability will be boosted. Conclusion: The purpose of this research is the application of artificial intelligence techniques in branch of dentistry to the aim of early diagnosis and treatment of oral diseases.
Full-Text [PDF 161 kb]   (1180 Downloads)    
Type of Study: Original article | Subject: Radiology
Received: 2013/11/14 | Accepted: 2013/11/17 | Published: 2013/11/17

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.