Yazar "Ayhan, Baturalp" seçeneğine göre listele

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    A novel deep learning-based perspective for tooth numbering and caries detection
    (Springer Heidelberg, 2024) Ayhan, Baturalp; Ayan, Enes; Bayraktar, Yusuf
    ObjectivesThe aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms.MethodsThe dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process.ResultsAccording to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively.ConclusionsThe proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently.Clinical significanceCNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.
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    Öğe
    Dental student application of artificial intelligence technology in detecting proximal caries lesions
    (Wiley, 2024) Ayan, Enes; Bayraktar, Yusuf; Celik, Cigdem; Ayhan, Baturalp
    Objectives: This study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.Methods: A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called You Only Look Once, was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre-test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post-test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.Results: When both groups' pre-test and post-test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non-trained group in terms of post-test scores (p < 0.05). In group 2 (trained group), the post-test labeling time was considerably increased (p < 0.05).Conclusions: The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.
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    Dijital bitewing radyografilerde derin öğrenme yöntemi ile dişlerin numaralandırılması ve çürük tespitinin değerlendirilmesi
    (Kırıkkale Üniversitesi, 2023) Ayhan, Baturalp; Bayraktar, Yusuf
    Bu tez çalışmasının amacı, derin öğrenme yöntemleri kullanılarak geliştirilen yapay zekâ uygulamaları ile dijital bitewing radyografilerdeki dişlerin numaralandırılması ve çürük tespit etkinliğinin gerçek zamanlı olarak değerlendirilmesidir. Bu tez çalışmasında kullanılan veri seti, Kırıkkale Üniversitesi Diş Hekimliği Fakültesi veri tabanından dahil edilme ve dışlama kriterlerine göre anonim olarak elde edilen 1170 adet dijital bitewing radyografiden oluşmaktadır. Bu veri setinin, 500 sağ ve 500 sol olmak üzere toplam 1000 adedi eğitim ve validasyon seti, geriye kalan 85 sağ ve 85 sol olmak üzere 170 adedi test veri seti olarak rastgele ayrılmıştır. Bu test veri seti, 1679 adet diş, 2842 adet aproksimal yüzey, 1679 adet numaralandırma etiketi ve 602 adet çürük etiketinden oluşmaktadır. Lezyon prevalansı %21,18'dir. Üç aşamadan oluşan yöntem bölümünün ilk aşamasında, bütün dijital bitewing radyografiler üzerinde etiketleme aracı kullanılarak iki diş hekiminin %100 mutabakatıyla numaralandırma ve çürük etiketlemeleri yapılmıştır. Daha sonra bir DenseNet-121 derin öğrenme modeli 1000 adet görüntü içeren eğitim veri seti ile eğitilmiştir. Bunun sonucunda model, 170 görüntüden oluşan test veri setini otomatik bir şekilde %100 doğrulukla sağ ve sol taraf olarak ayırmıştır. İkinci aşamada, geliştirilmiş bir YOLOv7 modeli 500 sağ ve 500 sol olmak üzere toplam 1000 görüntüden oluşan eğitim veri seti üzerinde ayrı ayrı eğitilmiştir. Eğitimi yapılan model, 85 sağ ve 85 sol olmak üzere toplam 170 görüntüden oluşan test veri seti üzerinde numaralandırma ve çürük tespit işlemlerini otomatik olarak yapmıştır. Üçüncü aşamada, numaralandırılmış dişler ile tespit edilmiş çürükler bir algoritma yardımıyla eşleştirilerek hangi numaralı dişte çürük tespit edildiği belirlenmiştir. Modellerin performansı karmaşıklık matrisi kullanılarak değerlendirilmiştir. Geliştirilmiş YOLOv7 modeli, diş tespiti için 0,994 duyarlılık (sensitivite), 0,987 kesinlik (precision) ve 0,990 F1-skoru; numaralandırma için 0,974 duyarlılık (sensitivite), 0,985 kesinlik (precision) ve 0,979 F1-skoru; çürük tespiti için 0,833 duyarlılık (sensitivite), 0,866 kesinlik (precision) ve 0,849 F1-skoru değerlerini elde etmiştir. Numaralandırılan diş ve tespit edilen çürüğün eşleştirilmesi için, 0,934 doğruluk (accuracy), 0,834 duyarlılık (sensitivite), 0,961 özgüllük (spesifite), 0,851 kesinlik (precision) ve 0,842 F1-skoru değerleri elde edilmiştir. Bu tez çalışmasına göre, derin öğrenme yönteminin bitewing radyografiler üzerinde numaralandırma ve çürük tespit etkinliğinin sonuçları umut vericidir.
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    Öğe
    The effect of SARS-CoV-2 effective mouthwashes on the staining, translucency and surface roughness of a nanofill resin composite
    (Mosher & Linder, Inc, 2021) Bayraktar, Yusuf; Karaduman, Kubra; Ayhan, Baturalp; Hendek, Meltem Karsiyaka
    Purpose: To evaluate the effect of SARS CoV-2 effective mouthwashes on the color change (Delta E), translucency parameter (TP) and average surface roughness (Ra) of a nanofill resin composite (Filtek Ultimate). Methods: 91 composite specimens (10 mm in diameter, 1 mm thickness) were prepared using a stainless-steel mold and randomly divided to seven groups as follows: Group 1 (CHX): 0.12% CHX digluconate + 0.15% benzydamine hydrochloride (96% alcohol), Group 2 (HAc): hypochlorous acid (500 ppm), Group 3 (PVP-I): 1% povidone iodine, Group 4 (H2O2): 1.5% hydrogen peroxide, Group 5 (CHX + C): 0.09% chlorhexidine digluconate + cyclodextrin + citrox, Group 6 (CPC): 0.075% cetylpyridinium hydrochloride, Group 7 (Control): artificial saliva. The initial color values were determined by a spectrophotometer on both white and black backgrounds. The specimens were immersed in 20 mL mouthwash for 1 minute with a 12-hour interval. The Ra, AE and TP were determined after 1, 2 and 3 weeks. Data were analyzed using repeated measures ANOVA, one-way ANOVA, post-hoc Bonferroni and Tukey tests (P< 0.05). Results: The highest and the lowest AE3 (after 3 weeks) value were observed in H2O2 (1.57 +/- 0.29) and CHX (0.92 +/- 0.17) groups, respectively. The CHX and CPC groups demonstrated significantly lower AE3 values than the control group (P< 0.05). CHX, HAc and PVP-I significantly affected the TP (P< 0.05). None of the groups demonstrated any significant changes of Ra scores (P> 0.05).