Artificial Intelligence Architectures Used in Dentistry
Özet
Artificial intelligence architectures represent important technological approaches that contribute to the restructuring of image-based evaluation processes in dentistry. Deep learning–based Convolutional Neural Networks enable the automatic extraction of local features from dental radiological data, while Vision Transformer–based models offer a different perspective for analyzing complex anatomical structures through their capacity to model global contextual relationships within images. Hybrid Convolutional Neural Network –Transformer architectures provide a more comprehensive evaluation framework by integrating both local and global representations. Segmentation-oriented architectures such as U-Net and its variants enable pixel-level delineation of anatomical and pathological structures, thereby supporting diagnostic interpretation and treatment planning processes. Object detection architectures contribute to the automatic identification of clinically relevant regions within dental images characterized by wide fields of view. Due to its reliance on high-resolution imaging data and detailed anatomical assessment, dentistry constitutes a distinctive domain for artificial intelligence applications. In this context, appropriate architectural selection, data quality, and accurate definition of clinical requirements emerge as key determinants for the reliable and sustainable integration of artificial intelligence systems into dental practice.
Yapay zekâ mimarileri, diş hekimliğinde görüntü temelli değerlendirme süreçlerinin yeniden yapılandırılmasına katkı sağlayan önemli teknolojik yaklaşımlar arasında yer almaktadır. Derin öğrenme tabanlı Konvolüsyonel Sinir Ağları, dental radyolojik verilerde lokal özelliklerin otomatik olarak çıkarılmasına olanak tanırken; Vision Transformer tabanlı modeller görüntü içerisindeki global bağlamsal ilişkileri modelleyebilme kapasitesiyle karmaşık anatomik yapıların analizine farklı bir perspektif kazandırmaktadır. CNN–Transformer hibrit mimariler ise lokal ve global temsilleri bir araya getirerek daha bütüncül bir değerlendirme imkânı sunmaktadır. Segmentasyon odaklı U-Net ve türevleri, anatomik ve patolojik yapıların piksel düzeyinde sınırlandırılmasını mümkün kılarak tanısal yorumlama ve tedavi planlaması süreçlerini desteklemektedir. Nesne tespiti mimarileri geniş görüş alanına sahip dental görüntülerde klinik açıdan anlamlı bölgelerin otomatik olarak belirlenmesine katkı sağlamaktadır Diş hekimliği pratiği, yüksek çözünürlüklü görüntüleme verileri ve ayrıntılı anatomik değerlendirme gereksinimi nedeniyle yapay zekâ uygulamaları için özgün bir çalışma alanı oluşturmaktadır. Bu bağlamda uygun mimari seçimi, veri kalitesi ve klinik gereksinimlerin doğru tanımlanması, yapay zekâ sistemlerinin güvenilir ve sürdürülebilir entegrasyonu açısından temel belirleyiciler olarak öne çıkmaktadır.
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