摘要
一位新闻记者兼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记者来自广州的新闻报道，研究表明：“宫颈癌是一个重大的全球性健康问题，其发病率和预后突出了早期筛查对有效预防的重要性，本研究旨在创建和验证一个用于宫颈细胞学分级的人工智能宫颈癌筛查(AICCS)系统。”我们的新闻编辑从中山大学的研究中获得了一句话：“AICCS系统是使用各种数据集训练和验证的，包括前瞻性、前瞻性和随机观察试验数据，总共涉及16056个数据。”研究对象：采用两种人工智能(AI)模型：一种是斑片级细胞检测模型，另一种是全片图像(WSIs)分类模型。AICCS在不同数据集上均显示出较高的预测肿瘤学分级的准确性，在前瞻性评估中，其曲线下面积(AUC)为0.947，敏感性0.946，特异性0.890，准确度0.892.随机观察试验显示，AICCS辅助的细胞病理学家比单独的细胞病理学家具有更高的AU C、特异性和准确性，敏感性显著提高13.3%。
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 from Guang zhou, People’s Republic of China, by NewsRx correspondents, research stated, “Ce rvical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This r esearch aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology.” Our news editors obtained a quote from the research from Sun Yat-sen University, “The AICCS system was trained and validated using various datasets, including r etrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) model s: one for detecting cells at the patch-level and another for classifying whole- slide image (WSIs). The AICCS consistently showed high accuracy in predicting cy tology grades across different datasets. In the prospective assessment, it achie ved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AU C, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity.”
NETL
NSTL