Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Prediction of Class III treatment outcomes through orthodontic data mining.

European journal of orthodontics - 1 Jun 2015

Auconi Pietro, Scazzocchio Marco, Cozza Paola, McNamara James A, Franchi Lorenzo

Abstract excerpt

OBJECTIVE: To determine whether it is possible to predict Class III treatment outcomes on the basis of a model derived from a combination of computational analyses derived from complexity science, such as fuzzy clustering repartition and network analysis. METHODS: Cephalometric data of 54 Class III patients (32 females, 22 males) taken before (T1, mean age 8.2 ± 1.6 years) and after (T2, mean age 14.6 ± 1.8...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cluster Analysis
Share to Cephalometry
Share to Cohort Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Cross-Sectional Studies

Share to Data Mining

Share to Forecasting

Share to Neural Networks, Computer

Share to Treatment Outcome

Source

Identifiers

PMID: 25190642
DOI: 10.1093/ejo/cju038

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Prediction of Class III treatment outcomes through orthodontic data mining.PMID 25190642Read ↗

Clustering and Machine Learning Models of Skeletal Class I and II Parameters of Arab Orthodontic PatientsSemantic proximity Read ↗

Machine Learning Models for Diagnosing Skeletal Class I and III in German Orthodontic PatientsSemantic proximity Read ↗

Explainable CBCT based morphometric clustering for orthodontic diagnostics using principal component analysis and k meansSemantic proximity Read ↗

CBCT-Based Morphometric Clustering for Orthodontic Diagnostics Using PCA and K-Means: An Explainable AI WorkflowSemantic proximity Read ↗

CBCT-Based Three-Dimensional Phenotyping of Skeletal Class II Malocclusion in Yemeni Adults: A Multivariate Workflow for AI-Ready Orthodontic DiagnosticsSemantic proximity Read ↗

CBCT-based three-dimensional phenotyping of skeletal Class II malocclusion in Yemeni adults: principal components and cluster analysisSemantic proximity Read ↗

Cone-Beam Computed Tomography-Based Three-Dimensional Phenotypes of Skeletal Class II Malocclusion in Yemeni Adults: A Principal Component and Cluster Analysis.Semantic proximity Read ↗

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 malocclusionSemantic proximity Read ↗

Unsupervised phenotyping of the periodontal architecture through high-dimensional clustering of electronic health records: A multicenter study.Semantic proximity Read ↗

Three-dimensional phenotype characteristics of skeletal class III malocclusion in adult Chinese: a principal component analysis-based cluster analysis.Semantic proximity Read ↗

Lateral Cephalometric Parameters Variations and Machine Learning Models Among Skeletal Class II & III Malocclusion of Arab Orthodontic PatientsSemantic proximity Read ↗

Comparative analysis of conditional generative adversarial network and Dolphin Visual Treatment Objective for soft tissue prediction in skeletal class I malocclusion extraction therapySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.