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

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

dc.contributor.author	Bittencourt, Marcos-Alan-Vieira	es
dc.contributor.author	de Sá Mafra, Pedro Henrique	es
dc.contributor.author	Julia, Roxanne Silva	es
dc.contributor.author	Travençolo, Bruno Augusto Nassif	es
dc.contributor.author	Silva, Pedro Urquiza Jayme	es
dc.contributor.author	Blumenberg, Cauane	es
dc.contributor.author	Silva, Virgínia Kelma dos Santos	es
dc.contributor.author	Paranhos, Luiz-Renato	es
dc.date.accessioned	2023-06-16T08:36:40Z
dc.date.available	2023-06-16T08:36:40Z
dc.date.issued	2021	es
dc.identifier.citation	Bittencourt, MA., Sá Mafra, PH., Julia, RS., Travençolo, BA., Silva, PU., Blumenberg, C., Silva, VK., & Paranhos, LR. (2021). Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review. En Medicina Oral Patología Oral y Cirugia Bucal (pp. e368-e378). Medicina Oral, S.L. https://doi.org/10.4317/medoral.24238	es
dc.identifier.uri	https://hdl.handle.net/10550/88034
dc.description.abstract	This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing odontogenic cysts. 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. 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. 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.	es
dc.subject	head and neck cancer	es
dc.subject	melanoma	es
dc.subject	oral melanoma	es
dc.subject	oral mucosa	es
dc.title	Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts:a systematic review	es
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO:CIENCIAS MÉDICAS	es
dc.identifier.doi	10.4317/medoral.24238	es
dc.type.hasVersion	VoR	es_ES
dc.identifier.url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141318/