Clustering Methods and Statistical Analysis of Clinical Data in Dentistry

Dear Colleagues,

The fundamental importance of accessing, interpreting, and using clinical data obtained by dentists to optimize dental care is well known. These data may be in the form of scientific papers, unstructured free-text medical records, or conference proceedings.

Statistical analysis of clinical data in dentistry is paramount for evidence-based practice and informed decision-making. Through rigorous statistical methods, dental professionals can elucidate trends, assess treatment efficacy, and identify potential risk factors. This not only enhances the quality of patient care but also contributes to the advancement of dental research, ultimately fostering improved oral health outcomes.

Specifically, clustering methods can be used to analyze complex data, identifying patterns and grouping similar cases together to obtain valuable information that can support clinical decisions. The applications of these methods include identifying patients with similar profiles based on shared dental concerns, responses to treatment, or risk factors to facilitate personalized interventions and tailored planning. Clustering also plays a crucial role in the classification of dental diseases, supporting the understanding of their progression, prognosis, and response to treatment. Finally, clustering techniques contribute to epidemiological research and studies in dentistry by identifying groups of patients with similar risk factors or disease patterns to discover etiological pathways and disease clusters in populations.

In this Special Issue, we will collect current scientific research on the application of clustering methods in extracting meaningful information from data in different dental fields in association with their clinical implications.

Dr. Fabiana Nicita
Guest Editor

Dr. Cinzia Di Nuzzo
Guest Editor Assistant

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