ArticlePloS one2012
Using networks to understand medical data: the case of Class III malocclusions.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Craniofacial growth predictors for class II and III malocclusions: A systematic review.Clinical and experimental dental research · 2021Pooled it
- Consequences of orthodontic treatment in malocclusion patients: clinical and microbial effects in adults and children.BMC oral health · 2016Trial
- Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns.BMC oral health · 2025Article
- Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms.Biomedical engineering online · 2025Article
- Network analysis of three-dimensional hard-soft tissue relationships in the lower 1/3 of the face: skeletal Class I-normodivergent malocclusion versus Class II-hyperdivergent malocclusion.BMC oral health · 2024Article
- A study of correlations between cephalometric measurements in Koreans with normal occlusion by network analysis.Scientific reports · 2024Article
- Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients.Scientific reports · 2019Article
- A Novel Technique to Overcome Treatment Delay Between Facemask Therapy and Comprehensive Orthodontic Correction.Journal of clinical and diagnostic research : JCDR · 2017Article
- Insights into Population Health Management Through Disease Diagnoses Networks.Scientific reports · 2016Article
- A network analysis of countries' export flows: firm grounds for the building blocks of the economy.PloS one · 2012Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A system of elements that interact or regulate each other can be represented by a mathematical object called a network. While network analysis has been successfully applied to high-throughput biological systems, less has been done regarding their application in more applied fields of medicine; here we show an application based on standard medical diagnostic data. We apply network analysis to Class III malocclusion, one of the most difficult to understand and treat orofacial anomaly. We hypothesize that different interactions of the skeletal components can contribute to pathological disequilibrium; in order to test this hypothesis, we apply network analysis to 532 Class III young female patients. The topology of the Class III malocclusion obtained by network analysis shows a strong co-occurrence of abnormal skeletal features. The pattern of these occurrences influences the vertical and horizontal balance of disharmony in skeletal form and position. Patients with more unbalanced orthodontic phenotypes show preponderance of the pathological skeletal nodes and minor relevance of adaptive dentoalveolar equilibrating nodes. Furthermore, by applying Power Graphs analysis we identify some functional modules among orthodontic nodes. These modules correspond to groups of tightly inter-related features and presumably constitute the key regulators of plasticity and the sites of unbalance of the growing dentofacial Class III system. The data of the present study show that, in their most basic abstraction level, the orofacial characteristics can be represented as graphs using nodes to represent orthodontic characteristics, and edges to represent their various types of interactions. The applications of this mathematical model could improve the interpretation of the quantitative, patient-specific information, and help to better targeting therapy. Last but not least, the methodology we have applied in analyzing orthodontic features can be applied easily to other fields of the medical science.
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