摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。根据NewsRx记者来自巴西戈尼亚尼亚的新闻报道，研究称：“评估人工智能(AI)在牙源性囊肿和肿瘤检测和分类方面的诊断能力，特别强调牙源性角化囊肿(OKC)和一例成纤维细胞瘤。我们查阅了9个电子数据库和灰色文献。”新闻记者引用了联邦大学Go IAS的一篇研究，“包括使用人工智能算法通过全景X线片或CBCT检测或分类牙源性囊肿和肿瘤的基于人类的研究，评估诊断试验，并对OKCs和成釉细胞瘤进行荟萃分析。”12项研究认为,人工智能是一种很有前途的病灶检测和/或分类工具,具有较高的诊断试验价值,3篇文章评价了卷积神经网络在全景X线片上对类似病变分类的敏感性。应用于锥束CT的AI仅在4项研究的基础上获得了较高的准确度，结果显示所用模型的异质性，影像学检查的差异性，以及与AI的相关性，其准确性为0.893(95%CI 0.832-0.954)。AI工具在OKC和造釉细胞瘤的检测和分类方面显示出相对较高的准确性。全景X线摄影似乎是一种基于AI的诊断这些病变的准确方法，尽管确定性低。
Abstract
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."
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