Turkiye Klinikleri Journal of Dental Sciences

.: ORIGINAL RESEARCH
Panoramik Radyografilerde Mandibular Kondil Morfolojisinin Belirlenmesinde Yapay Sinir Ağlarının Performansının Değerlendirilmesi: Metodolojik Çalışmalar
Evaluation of Performance of Artificial Neural Networks in Determination of Mandibular Condil Morphology in Panoramic Radiographies: Methodological Studies
Kader AZLAĞ PEKİNCEa , Buse Yaren KAZANGİRLERb , Adem PEKİNCEa
aKarabük Üniversitesi Diş Hekimliği Fakültesi, Ağız, Diş ve Çene Radyolojisi ABD, Karabük, Türkiye
bKastamonu Üniversitesi Taşköprü Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümü, Kastamonu, Türkiye
Turkiye Klinikleri J Dental Sci. 2024;30(1):40-8
doi: 10.5336/dentalsci.2023-99859
Article Language: TR
Full Text
ÖZET
 Amaç: Bu çalışmada; yapay sinir ağlarının (YSA), panoramik filmlerde mandibular kondil morfolojisini belirlemedeki performansının değerlendirilmesi amaçlanmıştır. Gereç ve Yöntemler: Çalışma için 18 yaş altı bireylere ait olan toplam 1.645 dijital panoramik görüntü incelendi. Bu görüntüler üzerinde sağ ve sol eklem olmak üzere toplam 3.290 mandibular kondil bölgesi kesilerek morfolojik açıdan değerlendirildi. Kesilen görüntüler normal ve anormal olarak etiketlenen kondil görüntüleri YSA modeline verilmek üzere %75 eğitim seti, %15 doğrulama seti ve %10 test seti olarak ayrıldı. Çalışmada, sinir ağı mimarisi olarak DenseNet mimarisi kullanıldı. Bulgular: Çalışma kapsamında, özellikle seçilen sinir ağı modeli ile eğitim aşaması için %91,76'ya ulaşırken, test aşaması için %89,00 doğruluk oranı ile yüksek performansa ulaştığı varsayılmıştır. Buna göre normal sınıfı için 197 adet normal etiketi test edilirken, 19 adet veride yanlış olarak anormal etiketi bulunmuştur. Bununla birlikte değerlendirme sırasında 96 adet anormal sınıfı doğru olarak test edilirken 17 adet veri ise normal olarak değerlendirilmiştir. Sonuç: Mandibular kondil morfolojisi, YSA kullanılarak yüksek oranda doğru tespit edilmiştir. İlerde yapılacak çalışmalarda veri sayısı artırılarak başarının daha da artırılması mümkündür. Temporomandibular eklem bölgesinin yapay zekâ destekli programlar tarafından yüksek doğrulukla değerlendirilebilmesi, klinikte çokça karşılaşılan bu grup hastaların doğru tanı almalarını hızlandıracak ve doğru yönlendirme ile daha çabuk tedavi imkânı bulmalarını kolaylaştıracaktır.

Anahtar Kelimeler: Panoramik radyografi; mandibular kondil; yapay zekâ; bilgisayarlı görü teknikleri
ABSTRACT
 Objective: In this study; it is aimed to evaluate the performance of artificial neural networks in determining the morphology of the mandibular condyle in panoramic images. Material and Methods: A total of 3,290 including right and left joints, mandibular condyles were cut and morphologically examined on 1,645 digital panoramic images. Condyle images that labeled as normal and abnormal were divided into 75% training set, 15% validation set and 10% test set to be given to the artificial neural network model. In this study, DenseNet architecture and GoogLeNet architectures was used as the neural network architecture. Results: Within the scope of the study, it was assumed that while it reached 91.76% for the training phase with the selected neural network model, it reached a high performance with an accuracy rate of 89.00% for the test phase. Accordingly, while 197 normal labels were tested for the normal class, 19 incorrectly abnormal labels were found in the data. However, during the evaluation, 96 classes of abnormal were tested correctly, while 17 data were evaluated as normal. Conclusion: A high rate of success has been achieved with the use of artificial neural networks in determining the morphology of the mandibular condyle. It is possible to further increase the success by increasing the number of data in future studies. Evaluating the temporomandibular joint region with high accuracy by artificial intelligence-supported programs will accelerate the correct diagnosis of this group of patients, who are frequently encountered in the clinic, and make it easier for them to get treatment more quickly with the right guidance.

Keywords: Panoramic radiography; mandibular condyle; artificial intelligence; computer vision techniques
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