Search Results - "Zahedrozegar, Samira"

Prevalence and Features of Maxillary Sinus Septa in Patients with Cleft Lip and Palate: Cone Beam Computed Tomography Imaging Technique by Paknahad, Maryam, Zahedrozegar, Samira, Babanouri, Neda, Ajami, Shabnam

“…Objective The development of the maxillary sinus is different in patients with cleft lip and palate (CLP) compared to non-CLP individuals. To investigate the…”
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Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy by Sadr, Soroush, Mohammad-Rahimi, Hossein, Motamedian, Saeed Reza, Zahedrozegar, Samira, Motie, Parisa, Vinayahalingam, Shankeeth, Dianat, Omid, Nosrat, Ali

“…The aim of this systematic review and meta-analysis was to investigate the overall accuracy of deep learning models in detecting periapical (PA) radiolucent…”
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Artificial Intelligence for Detection of External Cervical Resorption Using Label-Efficient Self-Supervised Learning Method by Mohammad-Rahimi, Hossein, Dianat, Omid, Abbasi, Reza, Zahedrozegar, Samira, Ashkan, Ali, Motamedian, Saeed Reza, Rohban, Mohammad Hossein, Nosrat, Ali

“…INTRODUCTIONThe aim of this study was to leverage label-efficient self-supervised learning (SSL) to train a model that can detect ECR and differentiate it from…”
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Deep learning in periodontology and oral implantology: A scoping review by Mohammad‐Rahimi, Hossein, Motamedian, Saeed Reza, Pirayesh, Zeynab, Haiat, Anahita, Zahedrozegar, Samira, Mahmoudinia, Erfan, Rohban, Mohammad Hossein, Krois, Joachim, Lee, Jae‐Hong, Schwendicke, Falk

“…Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and…”
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Artificial Intelligence for Detection of External Cervical Resorption Using Label-Efficient Self-Supervised Learning Method by Mohammad-Rahimi, Hossein, Dianat, Omid, Abbasi, Reza, Zahedrozegar, Samira, Ashkan, Ali, Motamedian, Saeed Reza, Rohban, Mohammad Hossein, Nosrat, Ali

“…The aim of this study was to leverage label-efficient self-supervised learning (SSL) to train a model that can detect ECR and differentiate it from caries…”
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