首页|Federal University Goias Reports Findings in Artificial Intelligence (Diagnostic capability of artificial intelligence tools for detecting and classifying odont ogenic cysts and tumors: a systematic review and meta-analysis)
Federal University Goias Reports Findings in Artificial Intelligence (Diagnostic capability of artificial intelligence tools for detecting and classifying odont ogenic cysts and tumors: a systematic review and meta-analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Goiania , Brazil, by NewsRx journalists, research stated, "To evaluate the diagnostic ca pability of artificial intelligence (AI) for detecting and classifying odontogen ic cysts and tumors, with special emphasis on odontogenic keratocyst (OKC) and a meloblastoma. Nine electronic databases and the gray literature were examined." The news reporters obtained a quote from the research from Federal University Go ias, "Humanbased studies using AI algorithms to detect or classify odontogenic cysts and tumors by using panoramic radiographs or CBCT were included. Diagnosti c tests were evaluated, and a meta-analysis was performed for classifying OKCs a nd ameloblastomas. Heterogeneity, risk of bias, and certainty of evidence were e valuated. Twelve studies concluded that AI is a promising tool for the detection and/or classification of lesions, producing high diagnostic test values. Three articles assessed the sensitivity of convolutional neural networks in classifyin g similar lesions using panoramic radiographs, specifically OKC and ameloblastom a. The accuracy was 0.893 (95% CI 0.832-0.954). AI applied to cone beam computed tomography produced superior accuracy based on only 4 studies. Th e results revealed heterogeneity in the models used, variations in imaging exami nations, and discrepancies in the presentation of metrics. AI tools exhibited a relatively high level of accuracy in detecting and classifying OKC and ameloblas toma. Panoramic radiography appears to be an accurate method for AI-based classi fication of these lesions, albeit with a low level of certainty."
GoianiaBrazilSouth AmericaAmelobla stomasArtificial IntelligenceCancerEmerging TechnologiesHealth and Medic ineJaw CystsJaw Diseases and ConditionsMachine LearningOdontogenic CystsOncology
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