ABSTRACT
Background: This study aimed to search for scientific evidence concerning the accuracy of computer-assistedanalysis for diagnosing odontogenic cysts.Material and Methods: A systematic review was conducted according to the PRISMA statements and consideringeleven databases, including the grey literature. Protocol was registered in PROSPERO (CRD 42020189349). ThePECO strategy was used to define the eligibility criteria and only studies involving diagnostic accuracy were in-cluded. 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 andwere 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 twosubtypes of odontogenic keratocysts (solitary or syndromic) were pooled together, the case-wise analysis showinga success rate of 100% for odontogenic keratocysts and radicular cysts, in one of them. In two studies, the den-tigerous cyst was associated with the majority of misclassifications, and its omission from the dataset improvedsignificantly the classification rates. Conclusions: The overall evaluation showed all studies presented high accuracy rates of computer-aided systems inclassifying odontogenic cysts in digital images of histological tissue sections. However, due to the heterogeneity ofthe studies, a meta-analysis evaluating the outcomes of interest was not performed and a pragmatic recommendationabout their use is not possible.(AU)
