ArticleRoyal Society open science2024
Features and networks of the mandible on computed tomography.
Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Pharyngeal Airway Changes After Functional Orthodontic Treatment in Growing Class II Patients: A Retrospective Cephalometric Comparison of Twin Block, RPE and AMCOPLife (Basel, Switzerland) · 2025Article
- Bibliometric Analysis of the Role of Artificial Intelligence in Detecting Maxillofacial Fractures.Cureus · 2024Review
- Isolated fracture of the coronoid process following a molar extraction: A rare case report.The Journal of international medical research · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mandible or lower jaw is the largest and hardest bone in the human facial skeleton. Fractures of the mandible are reported to be a common facial trauma in emergency medicine and gaining insights into mandibular morphology in different facial types can be helpful for trauma treatment. Furthermore, features of the mandible play an important role in forensics and anthropology for identifying gender and individuals. Thus, discovering hidden information of the mandible can benefit interdisciplinary research. Here, for the first time, a method of artificial intelligence-based nonlinear dynamics and network analysis are used for discovering dissimilar and similar radiographic features of mandibles between male and female subjects. Using a public dataset of 10 computed tomography scans of mandibles, the results suggest a difference in the distribution of spatial autocorrelation between genders, uniqueness in network topologies among individuals and shared values in recurrence quantification.
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Registered trials
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