Artificial Intelligence–Based Topic Modeling of Cadaveric Anatomy Literature: A 25-Year BERTopic Analysis (2000–2025)

Abstract

Aim: This study aimed to identify research topics in the cadaveric anatomy literature between 2000 and 2025, reveal semantic relationships between these topics, and analyze temporal trends. Methods: A systematic search of the Scopus database yielded 2,465 articles published between 2000 and 2025; after preprocessing, 2,455 were included. The Bidirectional Encoder Representations from Transformers Topic Modeling algorithm — integrating Sentence-BERT, Uniform Manifold Approximation and Projection, and Hierarchical Density-Based Spatial Clustering of Applications with Noise — was applied to article titles and abstracts. Topic coherence was assessed using the C_v score, and temporal trends were evaluated with the Mann-Kendall trend test. Results: A total of 62 topics were identified (mean C_v=0.664). Of all articles, 29.8% were classified as outliers. Eighteen topics showed a statistically significant increasing trend; no topic showed a decrease. The strongest increase was observed in sciatic nerve and related clinical anatomy topics. Education, endoscopic surgery, and vascular anatomy emerged as prominent thematic areas. The United States accounted for the largest share of publications (34.4%), with contributions from 60 countries overall. Conclusion: To our knowledge, this is the first transformer-based topic modeling study to map the cadaveric anatomy literature. The findings demonstrate significant growth in education-focused research alongside sustained expansion across surgical and clinical anatomy topics, confirming that cadaver studies maintain their central role in modern medical training and research.

Keywords

• Cadaver
• Dissection
• Anatomy
• Natural Language Processing
• Bibliometrics

Ethical Statement

This study was conducted through the analysis of publicly available data obtained from the Scopus database. Since the study did not involve human participants, personal data, or biological material, ethical approval from an institutional review board was not required.

Yapay Zeka Tabanlı Konu Modellemesi ile Kadaverik Anatomi Literatürünün Haritalanması: 25 Yıllık BERTopic Analizi (2000–2024)

Abstract

Amaç: Bu çalışma, 2000–2025 yılları arasındaki kadaverik anatomi literatüründeki araştırma konularını belirlemek, bu konular arasındaki semantik ilişkileri ortaya koymak ve zamansal eğilimleri analiz etmeyi amaçlamaktadır. Yöntem: Scopus veri tabanında gerçekleştirilen sistematik tarama sonucunda 2000–2025 yılları arasında yayınlanan 2.465 makale elde edilmiş; ön işleme sonrasında 2.455 makale analize dahil edilmiştir. Cümle düzeyinde anlamsal gömme modeli, Tekdüze Manifold Yaklaşımı ve Projeksiyon algoritması ile Hiyerarşik Yoğunluk Tabanlı Gürültülü Uygulamalar için Uzamsal Kümeleme algoritması bileşenlerini entegre eden Transformer Tabanlı Konu Modellemesi algoritması makale başlık ve özetlerine uygulanmıştır. Konu tutarlılığı C_v skoru ile değerlendirilmiş, zamansal eğilimler Mann-Kendall trend testi ile analiz edilmiştir. Bulgular: Toplam 62 konu belirlenmiştir (ortalama C_v=0,664). Makalelerin %29,8'i aykırı değer olarak sınıflandırılmıştır. On sekiz konu istatistiksel olarak anlamlı artış eğilimi gösterirken hiçbir konuda azalma gözlemlenmemiştir. En güçlü artış siyatik sinir ve ilgili klinik anatomi konularında saptanmıştır. Eğitim, endoskopik cerrahi ve vasküler anatomi öne çıkan tematik alanlar olarak belirlenmiştir. Yayınların en büyük payı Amerika Birleşik Devletleri'ne ait olup (%34,4), toplamda 60 farklı ülkeden katkı sağlanmıştır. Sonuç: Bilgimiz dahilinde bu çalışma, kadaverik anatomi literatürünü haritalayan ilk transformer tabanlı konu modelleme çalışmasıdır. Bulgular, eğitim odaklı araştırmalardaki belirgin büyümenin yanı sıra cerrahi ve klinik anatomi konularındaki süregelen genişlemeyi ortaya koymakta; kadaver çalışmalarının modern tıp eğitimi ve araştırmalarındaki merkezi rolünü korumaya devam ettiğini teyit etmektedir.

Keywords

• Kadavra
• Diseksiyon
• Anatomi
• Doğal Dil İşleme
• Bibliyometri

Ethical Statement

Bu çalışma, Scopus veri tabanından elde edilen kamuya açık verilerin analizi yoluyla gerçekleştirilmiştir. Çalışma insan katılımcıları, kişisel veriler veya biyolojik materyal içermediğinden, kurumsal etik kurulundan onay alınmasına gerek duyulmamıştır.

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