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    Anatomical and Morphological Assessment of Nasopalatine Canal in Pediatric and Adolescent Population via Cone Beam Computed Tomography
    (Lippincott Williams & Wilkins, 2021) Aydin, Kader Cesur; Gas, Selin
    Introduction: Anatomical and morphological structure of nasopalatine canal (NPC) is important for surgical techniques carried out on the maxilla. The aim of the present study was to analyze the anatomical and morphological characteristics of the NPC among pediatric and adolescent population using cone beam computed tomography (CBCT). Materials and Methods: A total of 437 cases were analyzed using CBCT images in this retrospective, cross-sectional study. Shape was analyzed as hourglass, cone, funnel, banana, cylindrical, and tree branch like. Number of foramina Stenson (FS) was evaluated through coronal, axial, and sagittal views. Landmark evaluation involved; diameter of FS, diameter of incisive foramen, diameter at the mid-canal length, NPC length, and narrowest buccal bone thickness. Pathology presence near NPC was evaluated to determine alterations on the landmark metrics. Results: Nasopalatine canal shape distribution revealed 32% hourglass, 9.6% conic, 10.8% funnel, 11.9% banana, 29.5% cylindrical and 6.2% tree branch. Number of FS (P = 0.021; P < 0.05), diameter of FS (P = 0.041; p < 0.05), NPC length (P: 0.020; P < 0.05), and narrowest buccal bone thickness from the mid-canal length was significantly higher in males (P: 0.000; P < 0.05). Diameter of incisive foramen and diameter at the mid-canal length revealed no significance among genders (P (1) = 0.318, P (2) = 0.105; P > 0.05). Incidence of pathology near NPC is 20.8% and was not affected by gender (P = 0,192; P > 0.05). Conclusions: The current study demonstrates significant variations of NPC morphology among patients. Therefore, CBCT analysis is highly recommended for clinicians to reduce the complications in oral and maxillofacial surgery practices and to provide better surgical outcomes.
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    Comparison of artificial intelligence vs. junior dentists' diagnostic performance based on caries and periapical infection detection on panoramic images
    (Ame Publishing Company, 2023) Gunec, Huseyin Gurkan; Urkmez, Elif Seyda; Danaci, Aleyna; Dilmac, Eda; Onay, Huseyin Hamza; Aydin, Kader Cesur
    Background: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists. Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists. Conclusions: Regarding the evaluation time needed, AI performed faster, on average.
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    Comparison of artificial intelligence vs. junior dentists' diagnostic performance based on caries and periapical infection detection on panoramic images
    (Ame Publishing Company, 2023) Gunec, Hueseyin Gurkan; Urkmez, Elif Seyda; Danaci, Aleyna; Dilmac, Eda; Onay, Hueseyin Hamza; Aydin, Kader Cesur
    Background: There is information missing in the literature about the comparison of dentists vs. artificial intelligence (AI) based on diagnostic capability. The aim of this study is to evaluate the diagnostic performance based on radiological diagnoses regarding caries and periapical infection detection by comparing AI software with junior dentists who have 1 or 2 years of experience, based on the valid determinations by specialist dentists. Methods: In the initial stage of the study, 2 specialist dentists evaluated the presence of caries and periapical lesions on 500 digital panoramic radiographs, and the detection time was recorded in seconds. In the second stage, 3 junior dentists and an AI software performed diagnoses on the same panoramic radiographs, and the diagnostic results and durations were recorded in seconds. Results: The AI and the three junior dentists, respectively, detected dental caries at a sensitivity (SEN) of 0.907, 0.889, 0.491, 0.907; a specificity (SPEC) of 0.760, 0.740, 0.454, 0.696; a positive predictive value (PPV) of 0.693, 0.470, 0.155, 0.666; a negative predictive value (NPV) of 0.505, 0.415, 0.275, 0.367 and a F1-score of 0.786, 0.615, 0.236, 0.768. The AI and the three junior dentists respectively detected periapical lesions at an SEN of 0.973, 0.962, 0.758, 0.958; a SPEC of 0.629, 0.421, 0.404, 0.621; a PPV of 0.861, 0.651, 0.312, 0.648; a NPV of 0.689, 0.673, 0.278, 0.546 and an F1-score of 0.914, 0.777, 0.442, 0.773. The AI software gave more accurate results, especially in detecting periapical lesions. On the other hand, in caries detection, the underdiagnosis rate was high for both AI and junior dentists. Conclusions: Regarding the evaluation time needed, AI performed faster, on average.
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    Quality of information on YouTube about artificial intelligence in dental radiology
    (Wiley, 2020) Aydin, Kader Cesur; Gunec, Huseyin Gurkan
    Objectives This study was designed to investigate Artificial Intelligence in Dental Radiology (AIDR) videos on YouTube in terms of popularity, content, reliability, and educational quality. Methods Two researchers systematically searched about AIDR on YouTube on January 27, 2020, by using the terms artificial intelligence in dental radiology, machine learning in dental radiology, and deep learning in dental radiology. The search was performed in English, and 60 videos for each keyword were assessed. Video source, content type, time since upload, duration, and number of views, likes, and dislikes were recorded. Video popularity was reported using Video Power Index (VPI). The accuracy and reliability of the source of information were measured using the adapted DISCERN score. The quality of the videos was measured using JAMAS and modified Global Quality Score (mGQS) and content via Total Concent Evaluation (TCE). Results There was high interobserver agreement for DISCERN (intraclass correlation coefficient [ICC]: 0.975; 95% confidence interval [CI]: 0.957-0.985; P: 0.000;P < 0.05) and mGQS (ICC: 0.904; 95% CI: 0.841-0.943; P: 0.000;P < 0.05). Academic source videos had higher DISCERN, GQS, and TCE, revealing both reliability and quality. Also, positive relationship of VPI with mGQS (30.1%) (P: 0.035) and DISCERN (38.1%) (P: 0.007) is detected. The scores revealed 51.9% relationship between mGQS and DISCERN (P: 0.001); and educational quality predictor scores revealed 62.5% relationship between TCE and GQS (P: 0.000). Conclusion Despite the limited number of relevant videos, YouTube involves reliable and quality videos that can be used by dentists about learning AIDR.