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。根据来自匈牙利布达佩斯的新闻，NewsRx记者报道，“这项研究的目的是评估基于AI的Diagnocat系统的可靠性，该系统有助于医疗保健过程在口腔内X线片上诊断龋齿。由两名独立的观察者使用基于AI的Diagnocat系统对323颗选定牙齿的近表面进行评估。”我们的新闻记者从Semmelweis Universsi Ty的研究中获得了一句话，“在1期中记录了龋损的存在或不存在。4个月后，人工智能辅助的人类观察者评估了相同的X线片（2期），高级卷积神经网络(CNN)重新评估了X线片HIC数据（3期）。随后，收集了反映人类分歧的数据（4期）。计算Cohen Kappa值和Fleiss Kappa值,以及诊断的敏感性、特异性、阳性预测值和阴性预测值及诊断的准确性,在四个阶段,人类观察者与诊断的Cohen Kappa值范围分别为k=0.66-1、k=0.58-0.7、k=0.49-0.7.,Fleiss Kappa值为k=0.57-0.8.,敏感性、特异性和诊断的准确性范围分别为0.51-0.76、0.88-0.97和0.76-0.86.Diagnocat CNN支持评价口腔内x线片诊断龋病，这是由人类和人工智能系统观察者一致确定的。
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 originating from Budapest, Hunga ry, by NewsRx correspondents, research stated, "This study aimed to assess the r eliability of AI-based Diagnocat system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independen t observers using the AI-based Diagnocat system." Our news journalists obtained a quote from the research from Semmelweis Universi ty, "The presence or absence of carious lesions was recorded during Phase 1. Aft er 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiograp hic data (Phase 3). Subsequently, data reflecting human disagreements were exclu ded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnost ic accuracy of Diagnocat, were calculated. During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were k=0.66-1, k=0. 58-0.7, and k=0.49-0.7. The Fleiss kappa values were k=0.57-0.8. The sensitivity , specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. The Diagnocat CNN supports the evaluation of intra oral radiographs for caries diagnosis, as determined by consensus between human and AI system observers."
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