Mapping the Scientific Evolution of Peyronie’s Disease: A Bibliometric and Machine Learning Analysis

Authors : Alper Şimşek, Mehmet Kırdar, Nart Görgü, Aykut Başer, Güngör Bingöl, Kadir Kesgin

Pages : 945-952

Doi:10.46413/boneyusbad.1763692

View : 59 | Download : 106

Publication Date : 2025-12-19

Article Type : Research Paper

Abstract :Aim: Peyronie’s disease is an acquired penile connective tissue disorder characterized by fibrous plaque formation, leading to curvature, pain, and sexual dysfunction. Research on peyronie’s disease has increased markedly in the past two decades, but variability in study quality complicates the identification of impactful work. This study aimed to perform a bibliometric analysis of peyronie’s disease-related publications to highlight research trends and guide future studies. Material and Method: A literature search was conducted on February 16, 2025, in Web of Science, Scopus, and TR Dizin using the terms “Peyronie’s disease,” “treatment of Peyronie’s disease,” “penile fibrosis,” and “tunica albuginea plaque.” Only English-language original research articles with “Peyronie” in the title were included. Data were analyzed with BibloX, assessing publication year, citations, journal frequency, and keyword trends. Citation forecasts were generated using machine learning models. Results: A total of 4,550 articles were identified, with the earliest in 1948. Publications rose sharply after 2000, particularly from the United States. The most cited study was Gelbard MK’s Natural History of Peyronie’s Disease (397 citations). Keyword analysis highlighted “Peyronie’s disease” and “erectile dysfunction.” Predictive models forecast varying 2026 citation counts. Conclusion: This analysis integrates machine learning algorithms into the bibliometric evaluation of Peyronie’s disease research, offering data-driven predictions and insights into future research directions.
Keywords : Peyroni hastalığı, Penis, Fibrozis, Bibliometrik analiz, Makine öğrenmesi

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