TRENDS IN DEEP AND MACHINE LEARNING FOR DENTAL LESION DETECTION: A BIBLIOMETRIC ANALYSIS (1991–2024) DENTAL LEZYONU TESPİTİ İÇİN DERİN ÖĞRENME VE MAKİNE ÖĞRENMESİNDEKİ TRENDLER: BİBLİYOMETRİK BİR ANALİZ (1991–2024)
Nobel Medicus, cilt.22, sa.2, ss.85-92, 2026 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 22 Sayı: 2
- Basım Tarihi: 2026
- Dergi Adı: Nobel Medicus
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, EMBASE, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO)
- Sayfa Sayıları: ss.85-92
- Anahtar Kelimeler: artificial intelligence, bibliometric analysis, bibliyometrik analiz, deep learning, Dental lesions, Dental lezyonlar, dental radiology, dental radyoloji, derin öğrenme, yapay zekâ
- Atatürk Üniversitesi Adresli: Evet
Özet
Objective: This study aims to evaluate the literature on the use of deep learning models in the diagnosis of dental lesions through bibliometric mapping analysis. Material and Method: Studies indexed in the Web of Science Core Collection database using relevant keywords were examined within the scope of bibliometric analysis. Publication year, citation numbers, countries, journals, authors, keywords, and collaboration networks were analyzed using VOSviewer software. Results: The analysis results showed that the number of publications on deep learning applications in the diagnosis of dental lesions increased significantly, especially after 2018. The keywords “deep learning”, “artificial intelligence”, and “machine learning” were found to have a central position in the literature. South Korea, China, and the United States were among the countries with the highest citations, while clinical and radiology-focused journals were found to be prominent in the field. Conclusion: This bibliometric analysis reveals research trends, prominent areas of study, and existing knowledge gaps regarding deep learning applications in the diagnosis of dental lesions. It is expected that the findings will guide future studies and contribute to the development of artificial intelligence-based diagnostic approaches in dental radiology.