English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the last decade, the availability of innovative algorithms derived from complexity theory has inspired the development of highly detailed models in various fields, including physics, biology, ecology, economy, and medicine. Due to the availability of novel and ever more sophisticated diagnostic procedures, all biomedical disciplines face the problem of using the increasing amount of information concerning each patient to improve diagnosis and prevention. In particular, in the discipline of orthodontics the current diagnostic approach based on clinical and radiographic data is problematic due to the complexity of craniofacial features and to the numerous interacting co-dependent skeletal and dentoalveolar components. In this study, we demonstrate the capability of computational methods such as network analysis and module detection to extract organizing principles in 70 patients with excessive mandibular skeletal protrusion with underbite, a condition known in orthodontics as Class III malocclusion. Our results could possibly constitute a template framework for organising the increasing amount of medical data available for patients' diagnosis

Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony