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Majid Akbary, Sanaz Kargozar,
Volume 29, Issue 2 (11-2016)
Volume 29, Issue 2 (11-2016)
Abstract
Background and Aims: Implementation of innovative methods for promoting learning persistence is of high importance. The aim of this study was to introduce an innovative teaching method in preclinical course of restorative dentistry and to investigate its impact on the students’ clinical skills.
Materials and Methods: In our study, which was done at the dental School of Mashhad University of Medical Sciences, the Team Based Learning (TBL) technique was applied. 48 fourth-year students took part in the course optionally. They were divided into four groups and the stages of TBL were performed. At the end of the course, a questionnaire was administered to the students. After passing the clinical course of restorative dentistry, the mean scores of students who had passed the voluntary preclinical course were compared with those of who did not pass this course. Data was analyzed using T-test.
Results: The results of this study revealed that the mean scores of students who had passed the voluntary preclinical course was higher than that of the students who did not pass the voluntary preclinical course (P=0.02). Moreover, the questionnaire showed that 97% of students were satisfied with this course and believed that it was useful for them.
Conclusion: This study showed that the innovative method of Team Based Learning (TBL) could promoted learning along with students’ satisfaction of dental preclinical courses.
Mohammad Hossein Ronaghi, Atefeh Bagheri,
Volume 36, Issue 0 (5-2023)
Volume 36, Issue 0 (5-2023)
Abstract
Background and Aims: Artificial intelligence (AI) technology is widely used in dentistry in addition to numerous other sectors that impact human life, including medicine. A dentist can use AI technology to analyze patient data, diagnostic processes, and management activities. This study was conducted in Iran to identify the dental applications of AI and prioritize them.
Materials and Methods: In the winter of 2022, this applied research was carried out in two stages using a mixed method. In the qualitative phase, 570 articles from 2011 to 2022 were identified in the databases of PubMed, Web of Science, Scopus and Google Scholar among the studies in the field of dentistry and related to artificial intelligence technology based on keywords and then the applications of artificial intelligence in dentistry were extracted. In the quantitative phase, the identified applications prioritized by a group of experts comprised 13 University faculty members with related research areas using the best-worst method (BWM).
Results: The factors identified in the first stage of research were classified into six categories: implant and surgery, executive management, disease diagnosis, analysis of images, clinical prediction, and orthodontics. According to the experts’ opinion, it was determined that medical photo analysis had the highest coefficient of importance (0.252) followed by orthodontics (0.234), disease diagnosis (0.151), implantology and surgery (0.143), clinical forecasts (0.127), and executive management (0.093).
Conclusion: Dentists can use the capabilities of artificial intelligence in examining patients' teeth and diagnostic tests in dentistry based on the analysis of patient information. Information technology policymakers with the support and reinforcement of knowledge-based companies active in the field of artificial intelligence and joint investment in the field of medicine can be the basis for progress and the development of this technology in the country and the field of treatment.
Materials and Methods: In the winter of 2022, this applied research was carried out in two stages using a mixed method. In the qualitative phase, 570 articles from 2011 to 2022 were identified in the databases of PubMed, Web of Science, Scopus and Google Scholar among the studies in the field of dentistry and related to artificial intelligence technology based on keywords and then the applications of artificial intelligence in dentistry were extracted. In the quantitative phase, the identified applications prioritized by a group of experts comprised 13 University faculty members with related research areas using the best-worst method (BWM).
Results: The factors identified in the first stage of research were classified into six categories: implant and surgery, executive management, disease diagnosis, analysis of images, clinical prediction, and orthodontics. According to the experts’ opinion, it was determined that medical photo analysis had the highest coefficient of importance (0.252) followed by orthodontics (0.234), disease diagnosis (0.151), implantology and surgery (0.143), clinical forecasts (0.127), and executive management (0.093).
Conclusion: Dentists can use the capabilities of artificial intelligence in examining patients' teeth and diagnostic tests in dentistry based on the analysis of patient information. Information technology policymakers with the support and reinforcement of knowledge-based companies active in the field of artificial intelligence and joint investment in the field of medicine can be the basis for progress and the development of this technology in the country and the field of treatment.
Firoozeh Nilchian, Saghar Salehi,
Volume 36, Issue 0 (5-2023)
Volume 36, Issue 0 (5-2023)
Abstract
Background and Aims: With the advancement of technology, the use of old teaching methods has been replaced by new and more efficient methods such as podcasts, to save the time of students and professors and to provide the opportunity for these two groups to interact more with be given to each other in the classroom. This method has advantages such as lower cost, the possibility of content courses, downloading content, and learning courses remotely. This study aimed to investigate the other benefits of this method as well as the level of students' satisfaction. Materials and Methods: In this study, which was a randomized, single-blind educational trial, the students at Dental School of Isfahan University of Medical Sciences were divided into two control and intervention groups (via a random number table), with 37 persons in each group. The classroom was held traditionally for both groups in 3 sessions and the podcast file was provided to the intervention group at the end of the session. Then, a test was taken from both groups at the second and third sessions. The students’ answers were evaluated and each score was determined out of 5. The scores of this test were compared in the intervention and control groups. Finally, a questionnaire was provided to students to evaluate their satisfaction. The test scores were analyzed by independent T-test. Results: In this study, with 74 participants, the average age was 22.47±0.85, and 43% of the participants were male. The scores of the two tests taken out of five were 4.86±0.10 in the intervention group and 4.04±0.94 in the control group, and a significant statistical difference was observed in favor of the intervention group (P=0.02). The average satisfaction of students regarding this educational method was 29.4 out of 50. After taking into account the confounding effect of age, the investigated intervention did not affect the average score of the students' questionnaire (P=0.031). Conclusion: According to the findings of the present study, podcasts could be effective in students' learning and students had a positive attitude towards this method. The results of this study can be used as a feedback to the educational committees of the Iranian Ministry of Health and Medical Education.
Hadi Kalani, Elham Abbasi,
Volume 38, Issue 0 (4-2025)
Volume 38, Issue 0 (4-2025)
Abstract
Background and Aims: Posterior crossbite is a common malocclusion disorder in the primary dentition that affects masticatory function. Therefore, early detection and treatment of crossbite teeth is essential to prevent further dental complications and guarantee proper jaw development. This study investigated a reasonable and computationally efficient diagnostic system for detecting characteristics between children with and without unilateral posterior crossbite in the primary dentition using the surface electromyography (sEMG) activity of masticatory muscles.
Materials and Methods: The present study was an experimental in vitro study that used sEMG signals and support vector machine (SVM) to develop artificial intelligence systems capable of decoding muscle activity for diagnosing the crossbite. The core idea of SVM is to find the optimal separating hyperplane that maximizes the margin between two classes (presence or absence of crossbite disease) in the sEMG signal. In this study, 40 children (4 to 6 years old) were selected and divided into unilateral posterior crossbite (UPCB) (n=20) and normal occlusion (n=20) groups. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. Then, the time domain and frequency domain features had been obtained. In this study, eighteen time domain features and nine frequency domain features were employed. Finally, these features were used as inputs to the SVM method for data classification and crossbite disease diagnosis. In this paper, four kernel functions of SVM including linear, 2nd order polynomial, 3rd order polynomial and radial basis function were considered.
Results: Based on the obtained results, the crossbite disease had a significant effect on the EMG signals. The results demonstrated that this disease affected the amplitude of the signal more than the frequency. Therefore, using the time features of EMG signals, the SVM method was able to provide a more accurate prediction of crossbite disease. The findings indicated that the mean absolute value feature achieved a 95% accuracy in predicting posterior crossbite. Finally, the results revealed that the RBF method could exhibit superior performance.
Conclusion: The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite. The findings of the study showed an influence of crossbite on the electrical activity of the temporal and masseter muscles. Therefore, the crossbite problem can be reasonably diagnosed by an appropriate learning strategy using EMG signals.
Materials and Methods: The present study was an experimental in vitro study that used sEMG signals and support vector machine (SVM) to develop artificial intelligence systems capable of decoding muscle activity for diagnosing the crossbite. The core idea of SVM is to find the optimal separating hyperplane that maximizes the margin between two classes (presence or absence of crossbite disease) in the sEMG signal. In this study, 40 children (4 to 6 years old) were selected and divided into unilateral posterior crossbite (UPCB) (n=20) and normal occlusion (n=20) groups. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. Then, the time domain and frequency domain features had been obtained. In this study, eighteen time domain features and nine frequency domain features were employed. Finally, these features were used as inputs to the SVM method for data classification and crossbite disease diagnosis. In this paper, four kernel functions of SVM including linear, 2nd order polynomial, 3rd order polynomial and radial basis function were considered.
Results: Based on the obtained results, the crossbite disease had a significant effect on the EMG signals. The results demonstrated that this disease affected the amplitude of the signal more than the frequency. Therefore, using the time features of EMG signals, the SVM method was able to provide a more accurate prediction of crossbite disease. The findings indicated that the mean absolute value feature achieved a 95% accuracy in predicting posterior crossbite. Finally, the results revealed that the RBF method could exhibit superior performance.
Conclusion: The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite. The findings of the study showed an influence of crossbite on the electrical activity of the temporal and masseter muscles. Therefore, the crossbite problem can be reasonably diagnosed by an appropriate learning strategy using EMG signals.
Mitra Montazerlotf, Mehrdad Mehrdad Hosseini Shakib, Reza Radfar, Mina Khayamzadeh,
Volume 38, Issue 0 (4-2025)
Volume 38, Issue 0 (4-2025)
Abstract
Background and Aims: Dental caries is one of the most prevalent chronic oral diseases worldwide. Timely and accurate diagnosis of dental caries plays a crucial role in preventing lesion progression and reducing complications. This study aimed to systematically review the studies on dental caries detection using machine learning algorithms applied to periapical radiographs.
Materials and Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar databases up to the end of 2024. Inclusion criteria comprised studies using machine learning algorithms for detecting dental caries in periapical or intraoral radiographs. The quality of studies was assessed using the QUADAS-2 tool.
Results: From 825 initial articles, 13 studies met the inclusion criteria. All studies used Convolutional Neural Networks (CNNs) with various architectures including ResNet, VGG, Inception, DenseNet, and YOLO. ResNet-based models and their hybrid variants showed the best performance with diagnostic accuracy ranging from 82% to 98%. Comparison with human experts in 6 studies revealed that deep learning algorithms demonstrated similar or superior performance.
Conclusion: From the results, deep learning especially convolutional neural networks, had significant potential for improving dental caries detection in periapical radiographs. However, challenges such as limited high-quality training data and generalizability issues need further investigation.
Materials and Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar databases up to the end of 2024. Inclusion criteria comprised studies using machine learning algorithms for detecting dental caries in periapical or intraoral radiographs. The quality of studies was assessed using the QUADAS-2 tool.
Results: From 825 initial articles, 13 studies met the inclusion criteria. All studies used Convolutional Neural Networks (CNNs) with various architectures including ResNet, VGG, Inception, DenseNet, and YOLO. ResNet-based models and their hybrid variants showed the best performance with diagnostic accuracy ranging from 82% to 98%. Comparison with human experts in 6 studies revealed that deep learning algorithms demonstrated similar or superior performance.
Conclusion: From the results, deep learning especially convolutional neural networks, had significant potential for improving dental caries detection in periapical radiographs. However, challenges such as limited high-quality training data and generalizability issues need further investigation.
Bita Kheiri, Mona Fazel Ghaziani,
Volume 39, Issue 0 (3-2026)
Volume 39, Issue 0 (3-2026)
Abstract
Background and Aims: In recent years, the use of artificial intelligence (AI) has become increasingly common in dentistry because it facilitates the process of diagnosis and clinical decision-making. It is necessary for dentists to be aware of the advantages and disadvantages of artificial intelligence before implementing it. The present study aimed to comprehensively review the various applications of artificial intelligence in the diagnosis of dental diseases along with its challenges and disadvantages.
Materials and Methods: For this review article, a complete search was conducted on the PubMed and Google Scholar databases and studies published in recent years as well as studies published in 2024 were collected using the keywords "artificial intelligence," "dentistry," "diagnosis." Finally, the relevant articles were selected and evaluated, focusing on artificial intelligence in dentistry and the diagnosis of dental diseases.
Results: Advances in artificial intelligence in dental imaging, particularly through machine learning (ML) and artificial neural networks (ANN), have dramatically transformed the way dental disease is diagnosed. These technologies help dentists to analyze complex information and produce more accurate results by using algorithms that allow systems to learn and respond to data. The most recent development in this area is deep learning (DL), which uses multiple layers of neural networks to process unlabeled data and predict outcomes. These techniques are used in various fields such as diagnostic imaging, periodontology, dental caries detection, and osteoporosis screening, which help to improve the quality of dental services. Despite the benefits of AI in clinical dentistry, three controversial challenges remain and need to be addressed: ease of use, return on investment, and evidence of performance, or reliability.
Conclusion: Based on the results, the most important advantage of AI is the diagnosis of dental diseases. AI has great potential to reduce the pressure on health systems by automating routine tasks and improving patient care. However, this technology can never replace human expertise and must be guided by ethical principles. Ultimately, AI is recognized as a valuable tool in dentistry and the final decision-making always remains with the dentist.
Materials and Methods: For this review article, a complete search was conducted on the PubMed and Google Scholar databases and studies published in recent years as well as studies published in 2024 were collected using the keywords "artificial intelligence," "dentistry," "diagnosis." Finally, the relevant articles were selected and evaluated, focusing on artificial intelligence in dentistry and the diagnosis of dental diseases.
Results: Advances in artificial intelligence in dental imaging, particularly through machine learning (ML) and artificial neural networks (ANN), have dramatically transformed the way dental disease is diagnosed. These technologies help dentists to analyze complex information and produce more accurate results by using algorithms that allow systems to learn and respond to data. The most recent development in this area is deep learning (DL), which uses multiple layers of neural networks to process unlabeled data and predict outcomes. These techniques are used in various fields such as diagnostic imaging, periodontology, dental caries detection, and osteoporosis screening, which help to improve the quality of dental services. Despite the benefits of AI in clinical dentistry, three controversial challenges remain and need to be addressed: ease of use, return on investment, and evidence of performance, or reliability.
Conclusion: Based on the results, the most important advantage of AI is the diagnosis of dental diseases. AI has great potential to reduce the pressure on health systems by automating routine tasks and improving patient care. However, this technology can never replace human expertise and must be guided by ethical principles. Ultimately, AI is recognized as a valuable tool in dentistry and the final decision-making always remains with the dentist.
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