Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review.
We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Sc...
| Publicado en: | Caries Research Vol. 56; no. 3; pp. 161 - 171 |
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| Autores principales: | , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Karger AG
2022
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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=160134638&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160134638 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00086568 DEU jtl: Caries Research issn: 00086568 maglogo: N pubinfo: dt: 2022 vid: 56 iid: 3 pid: 2485 pub: Karger AG artinfo: ui: 160134638 160134638 NLM35636386 160134638 10.1159/000524167 NLM35636386 160134638 ppf: 161 ppct: 10 formats: tig: atl: Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review. aug: au: Reyes, Lilian Toledo Knorst, Jessica Klöckner Ortiz, Fernanda Ruffo Ardenghi, Thiago Machado affil: Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil sug: subj: Dental Caries Diagnosis Cross Sectional Studies Algorithms Prognosis Systematic Review Human ab: We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Scopus, EMBASE, Web of Science, and grey literature until December 2020. We excluded review articles, case series, case reports, editorials, letters, comments, educational methodologies, assessments of robotic devices, and articles with less than 10 participants or specimens. Two independent reviewers selected the studies and performed the assessment of the methodological quality based on standardized scales. We summarize data on the machine learning algorithms used; software; performance outcomes such as accuracy/precision, sensitivity/recall, specificity, area under the receiver operating characteristic curve (AUC), and positive/negative predictive values related to dental caries. Meta-analyses were not performed due to methodological differences. Our review included 15 studies (10 diagnostic studies and 5 prognostic prediction studies). Cross-sectional design studies were predominant (12). The most frequently used statistical measure of performance reported in diagnostic studies was AUC value, which ranged from 0.745 to 0.987. For most diagnostic studies, data from contingency tables were not available. Reported sensitivities were higher in low risk of bias prognostic prediction studies (median [IQR] of 0.996 [0.971-1.000] vs. unclear/high risk of bias studies 0.189 [0-0.340]; p value 0.025). While there were no significant differences in the specificity between these subgroups, we concluded that the use of these technologies for the diagnosis and prognostic prediction of dental caries, although promising, is at an early stage. The general applicability of the evidence was limited given that most models were developed outside the real clinical setting with a prevalence of unclear/high risk of bias. Researchers must increase the overall quality of their research protocols by providing a comprehensive report on the methods implemented. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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