Radyasyon Onkolojisinde Makine Öğrenmesi

Öz

Yapay zeka (YZ), belirli görevleri yerine getirmek için doğrudan insan uyaranları olmadan bilgisayar yazılımı ve algoritmaları kullanan makinelerde insan benzeri zekayı taklit etmeye çalışan bir bilgisayar bilimidir. Makine öğrenimi (MÖ), önceki bir örneğe veya deneyime dayanarak insan davranışını taklit etmeyi öğrenen veri odaklı algoritmalar kullanan yapay zekanın alt birimidir. Derin öğrenme (DÖ), bir model oluşturmak için derin sinir ağlarını kullanan bir MÖ tekniğidir. Verilerin büyümesi ve paylaşımı, artan bilgi işlem gücü ve MÖ'deki gelişmeler sağlık hizmetlerinde bir dönüşüm başlatmıştır. Radyasyon onkolojisindeki ilerlemeler, her fraksiyon öncesi yapılan bilgisayarlı tomografi (BT) görüntülemesi, dozimetri ve görüntüleme ile entegre edilmesi gereken önemli miktarda veri üretmiştir. Radyasyon Onkolojisinde kullanılan birçok algoritma vardır. Bu yöntemlerin her birinin farklı hesaplama gücü gereksinimleriyle avantajları ve sınırlamaları vardır.Bu derlemede, radyoterapi (RT) sürecinin, MÖ ile kalitesinin ve verimliliğinin arttırılabileceği belirli alanları belirleyerek iş akışı sırası ile gözden geçirme amaçlanmıştır. RT aşaması, hasta değerlendirmesi, simülasyon, konturlama, planlama, kalite kontrol, tedavi uygulama ve hasta takibi olarak yedi gruba ayrılmıştır. Her aşamaya MÖ algoritmalarının uygulanabilirliği, sınırlamaları, avantajları ile ilgili sistematik bir değerlendirme yapılmıştır.

Anahtar Kelimeler

• radyoterapi,makine öğrenmesi,derin öğrenme,yapay zeka

Machine Learning in Radiation Oncology

Öz

Artificial intelligence (AI) is a computer science that tries to imitate human-like intelligence on machines using computer software and algorithms without direct human stimuli to perform certain tasks. Machine learning (ML) is the subunit of AI that uses data-driven algorithms that learn to emulate human behavior based on a previous example or experience. Deep learning (DL) is an ML technique that utilizes deep neural networks to construct a model. The growth and sharing of data, increased computing power, and developments in ML have initiated a transformation in healthcare. Advances in radiation oncology have generated substantial data that must be integrated with computed tomography (CT) imaging, dosimetry, and other imaging modalities before each fraction. There are many algorithms used in Radiation Oncology. Each of these methods has advantages and limitations and different computing requirements. In this paper, we aimed to review the radiotherapy (RT) process by identifying the specific areas in which the quality and efficiency of ML can be increased and a workflow chart can be created. The RT stage is divided into seven groups as patient assessment, simulation, contouring, planning, quality assessment (QA), treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations and advantages of ML algorithms was performed at each stage.

Anahtar Kelimeler

• Radiotherapy,machine learning,deep learning,artificial intelligence

Kaynakça

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