An artificial multilayer perceptron neural network for diagnosis of proximal dental caries.
OBJECTIVE: To evaluate if the application of an artificial intelligence model, a multilayer perceptron neural network, improves the radiographic diagnosis of proximal caries. STUDY DESIGN: One hundred sixty radiographic images of proximal surfaces of extracted human teeth were assessed regarding the...
| Publicado en: | Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology & Endodontology Vol. 106; no. 6; pp. 879 - 885 |
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| Autores principales: | , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Dec2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105602860&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105602860 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10792104 1VC jtl: Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology & Endodontology issn: 10792104 maglogo: N pubinfo: dt: Dec2008 vid: 106 iid: 6 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 105602860 2010141474 10.1016/j.tripleo.2008.03.002 NLM18718785 105602860 ppf: 879 ppct: 6 formats: tig: atl: An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. aug: au: Devito KL de Souza Barbosa F Filho WNF affil: Professor, Faculty of Dentistry, Federal University of Juiz De Fora, Juiz de Fora, Brazil. sug: subj: Dental Caries Radiography Neural Networks (Computer) Radiographic Image Interpretation, Computer-Assisted Methods Multilayer Perceptrons Dental Models Evaluation Research Interrater Reliability Outcomes Research Radiography, Bitewing Reproducibility of Results ROC Curve Sample Size Human ab: OBJECTIVE: To evaluate if the application of an artificial intelligence model, a multilayer perceptron neural network, improves the radiographic diagnosis of proximal caries. STUDY DESIGN: One hundred sixty radiographic images of proximal surfaces of extracted human teeth were assessed regarding the presence of caries by 25 examiners. Examination of the radiographs was used to feed the neural network, and the corresponding teeth were sectioned and assessed under optical microscope (gold standard). This gold standard served to teach the neural network to diagnose caries on the basis of the radiographic exams. To gauge the network's capacity for generalization, i.e., its performance with new cases, data were divided into 3 subgroups for training, test, and cross-validation. The area under the receiver operating characteristic (ROC) curve allowed comparison of efficacy between network and examiner diagnosis. RESULTS: For the best of the 25 examiners, the ROC curve area was 0.717, whereas network diagnosis achieved an ROC curve area of 0.884, indicating a sizeable improvement in proximal caries diagnosis. CONCLUSION: Considering all examiners, the diagnostic improvement using the neural network was 39.4%. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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