Son 10 Yılda Abdomen BT Görüntülemede Kullanılan Yapay Zeka Destekli Yayınların Bibliyografik Analizi

Abstract

Amaç: Bu çalışma, son on yılda abdomen bilgisayarlı tomografi (BT) alanında yapay zeka (YZ) destekli yayınların bibliyometrik analizini sunmaktadır. Yayın eğilimleri, atıf modelleri ve araştırma iş birliklerini inceleyerek, YZ’nin abdomen görüntülemedeki gelişen etkisine dair içgörüler sağlamayı amaçlamaktadır. Gereç ve Yöntemler: Veriler, 2014–2024 yılları için belirli arama kriterleri kullanılarak Web of Science Core Collection’dan alınmıştır. Bibliyometrik analiz, VOSviewer kullanılarak eş görülme ağları, atıf haritaları ve iş birliği modellerini oluşturmak amacıyla gerçekleştirilmiştir. Çalışma kapsamında anahtar kelime analizi, ortak yazarlık analizi, ortak atıf analizi ve bibliyografik eşleşme analizleri yapılmıştır. Bulgular: Son yıllarda abdomen BT’de YZ ile ilgili yayınlarda önemli bir artış gözlenmiş ve derin öğrenme baskın metodoloji olarak öne çıkmıştır. Atıf ağı analizi, görüntü rekonstrüksiyonu, segmentasyon ve radyomikler üzerine odaklanan temel çalışmaları belirlemiştir. İş birliği ağları, özellikle Amerika Birleşik Devletleri, Çin ve Güney Kore’deki kurumlar arasında güçlü uluslararası ve kurumsal ortaklıkları ortaya koymuştur. Ayrıca, GE Healthcare gibi endüstri-akademi iş birlikleri, abdomen görüntülemede YZ’nin ilerlemesine önemli katkılar sağlamıştır. Sonuçlar: YZ destekli abdomen BT görüntüleme, artan disiplinler arası iş birlikleri ile gelişmeye devam eden kritik bir araştırma alanıdır. Derin öğrenme ve radyomikler, klinik karar destek sistemleri ve kantitatif görüntüleme analizlerini şekillendiren temel odak noktaları haline gelmiştir. Gelecekteki araştırmalar, YZ’nin rutin radyoloji pratiğine entegrasyonunu ve geniş ölçekli doğrulama çalışmaları ile klinik etkinliğinin araştırılmasını önceliklendirmelidir.

Keywords

• Yapay zeka
• derin öğrenme
• makine öğrenmesi
• abdomen BT
• abdomen görüntüleme
• radyomikler

Bibliographic Analysis of Artificial Intelligence-Assisted Publications Used in Abdominal CT Imaging in the Last 10 Years

Abstract

Aim: This study presents a bibliometric analysis of artificial intelligence (AI)-)-assisted publications in abdominal computed tomography (CT) over the past decade. By examining publication trends, citation patterns, and research collaborations, this study offers insights into the evolving impact of AI in abdominal imaging. Materials and Methods: Data were retrieved from the Web of Science Core Collection using specific search criteria for 2014–2024. Bibliometric analysis was conducted using VOSviewer to generate co-occurrence networks, citation maps, and collaboration patterns. The study included keyword analysis, co-authorship analysis, co-citation analysis, and bibliographic coupling. Results: A significant increase in AI-related publications in abdominal CT has been observed in recent years, with deep learning emerging as the dominant methodology. Citation network analysis identified key studies focused on image reconstruction, segmentation, and radiomics. Collaboration networks highlighted strong international and inter-institutional partnerships, particularly among institutions in the United States, China, and South Korea. Additionally, industry-academic collaborations, notably with GE Healthcare, have contributed to the advancement of AI in abdominal imaging. Conclusions: AI-assisted abdominal CT imaging continues to expand as a critical area of research, demonstrating increasing interdisciplinary collaborations. Deep learning and radiomics have become focal points, influencing clinical decision support and quantitative imaging analysis. Future research should prioritize AI integration into routine radiology practice and explore its clinical effectiveness through large-scale validation studies.

Keywords

• Artificial intelligence
• deep learning
• machine learning
• abdominal CT
• abdominal imaging
• radiomics

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