Fractal analysis in dentistry: a bibliometric analysis (1991-2024).
Authors
Affiliations (2)
Affiliations (2)
- Faculty of Dentistry, Department of Pediatric Dentistry, Niğde Ömer Halisdemir University, 51240, Niğde, Turkey. [email protected].
- Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Necmettin Erbakan University, Meram, 42050, Konya, Turkey.
Abstract
This study aims to provide a comprehensive bibliometric mapping of the scientific evolution and research trends of fractal analysis (FA) in dentistry. The annual scientific production exhibited a dramatic surge post-2017, strongly correlating with the digital transition in dentomaxillofacial imaging. Oral Radiology and Dentomaxillofacial Radiology emerged as the core contributing journals, while Jurczyszyn K. and Bayrak S. were identified as the most impactful and productive authors, respectively. Citation analysis identified the seminal work by White et al., which investigated osteoporotic trabecular alterations, as the most highly cited article, highlighting the historical dominance of bone quality assessments in this domain. Geographically, a profound polarization was observed: Türkiye dominated the number of publications (n=90), whereas the USA achieved the highest global citation impact, driven by its extensive multiple-country publication networks. Thematic mapping based on author keywords revealed a comprehensive conceptual expansion across the domain; while "osteoporosis" and "fractal dimension" firmly anchor the field as foundational and motor themes, the literature is rapidly diversifying into specialized multidisciplinary applications and emerging computational niches such as texture analysis. FA has evolved from a theoretical mathematical concept into an indispensable, multidisciplinary diagnostic tool in modern dentistry. However, its full clinical integration is currently hindered by manual region of interest selection and algorithmic variability. The future trajectory of the domain is inextricably linked to the integration of FA into multidimensional radiomic workflows and the development of automated, artificial intelligence-driven diagnostic modules. Bridging these computational gaps is essential for establishing FA as a standardized pillar of precision diagnostics in the era of digital dentistry.