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Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review

BACKGROUND: This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing odontogenic cysts. MATERIAL AND METHODS: A systematic review was conducted according to the PRISMA statements and considering eleven databases, including the grey liter...

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Detalles Bibliográficos
Autores principales: Bittencourt, Marcos Alan Vieira, de Sá Mafra, Pedro Henrique, Julia, Roxanne Silva, Travençolo, Bruno Augusto Nassif, Silva, Pedro Urquiza Jayme, Blumenberg, Cauane, Silva, Virgínia Kelma dos Santos, Paranhos, Luiz Renato
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medicina Oral S.L. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141318/
https://www.ncbi.nlm.nih.gov/pubmed/33247568
http://dx.doi.org/10.4317/medoral.24238
Descripción
Sumario:BACKGROUND: This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing odontogenic cysts. MATERIAL AND METHODS: A systematic review was conducted according to the PRISMA statements and considering eleven databases, including the grey literature. Protocol was registered in PROSPERO (CRD 42020189349). The PECO strategy was used to define the eligibility criteria and only studies involving diagnostic accuracy were included. Their risk of bias was investigated using the Joanna Briggs Institute Critical Appraisal tool. RESULTS: Out of 437 identified citations, five papers, published between 2006 and 2019, fulfilled the criteria and were included in this systematic review. A total of 5,264 images from 508 lesions, classified as radicular cyst, odontogenic keratocyst, lateral periodontal cyst, glandular odontogenic cyst, or dentigerous cyst, were analyzed. All selected articles scored low risk of bias. In three studies, the best performances were achieved when the two subtypes of odontogenic keratocysts (solitary or syndromic) were pooled together, the case-wise analysis showing a success rate of 100% for odontogenic keratocysts and radicular cysts, in one of them. In two studies, the dentigerous cyst was associated with the majority of misclassifications, and its omission from the dataset improved significantly the classification rates. CONCLUSIONS: The overall evaluation showed all studies presented high accuracy rates of computer-aided systems in classifying odontogenic cysts in digital images of histological tissue sections. However, due to the heterogeneity of the studies, a meta-analysis evaluating the outcomes of interest was not performed and a pragmatic recommendation about their use is not possible. Key words:Computer-assisted diagnosis, computer-assisted image analysis, computer-assisted image processing, odontogenic cysts, keratocysts, radicular cysts.