The Contribution of Artificial Intelligence-Assisted Imaging Technologies to Radiation Safety in Pediatric Dentistry
Özet
This chapter explores the transformative role of artificial intelligence (AI) in enhancing radiation safety within pediatric dentistry. Children possess heightened radiosensitivity and a longer life expectancy, making cumulative ionizing radiation exposure from diagnostic imaging a critical public health concern. While the diagnostic paradigm has shifted towards the indication-oriented and patient-specific ALADAIP principle, clinicians face strict physical limitations, such as quantum mottle (noise), when attempting to reduce radiation doses.To overcome this fundamental barrier, the chapter details the shift towards physics-driven computational radiology, highlighting how deep learning architectures can mathematically decouple image fidelity from the radiation dose. Key AI interventions discussed include Generative Adversarial Networks (GANs) for low-dose image restoration and denoising, super-resolution algorithms to recover intricate spatial details, and synthetic imaging for artifact reduction without re-exposure. Ultimately, these advanced AI-assisted technologies serve as an invisible computational shield, ensuring uncompromised diagnostic clarity while maintaining the lowest possible biological risk for paediatric patients.
Bu bölüm, yapay zeka (YZ) destekli görüntüleme teknolojilerinin çocuk diş hekimliğinde radyasyon güvenliğini artırmadaki dönüştürücü rolünü incelemektedir. Çocukların yüksek radyosensitivitesi ve uzun yaşam beklentileri, tanısal görüntülemeden kaynaklanan kümülatif iyonlaştırıcı radyasyon maruziyetini kritik bir halk sağlığı sorunu haline getirmektedir. Teşhis paradigması hastaya özgü ALADAIP prensibine doğru kayarken, klinisyenler radyasyon dozunu düşürmeye çalıştıklarında kuantum gürültüsü gibi fiziksel engellerle karşılaşmaktadır. Bu temel engeli aşmak amacıyla bölüm, fiziğe dayalı hesaplamalı radyolojiye geçişi ele almakta ve derin öğrenme mimarilerinin görüntü kalitesini radyasyon dozundan matematiksel olarak nasıl bağımsız hale getirdiğini vurgulamaktadır. Düşük dozlu görüntülerin restorasyonu için Üretken Karşıt Ağlar (GAN), karmaşık uzamsal detayları geri kazandıran süper çözünürlük algoritmaları ve tekrar radyograf alımına gerek kalmadan artefakt azaltımı sağlayan sentetik görüntüleme gibi temel YZ müdahaleleri tartışılmaktadır. Sonuç olarak, bu gelişmiş YZ destekli teknolojiler görünmez dijital bir kalkan görevi görerek, pediyatrik hastalar için en düşük biyolojik riskle tanısal netlik sağlamaktadır.
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