PDF(Pubmed)
Abstract：
Diagnosing endometrial carcinoma correctly is essential for appropriate treatment, as it is a major health risk. As machine learning (ML) and artificial intelligence (AI) have grown in popularity, so has interest in their potential to improve cancer diagnosis accuracy. In the context of endometrial cancer, this study attempts to examine the efficacy as well as the accuracy of AI-assisted diagnostic approaches. Additionally, it aims to methodically evaluate the contribution of AI and ML techniques to the improvement of endometrial cancer diagnosis. Following PRISMA guidelines, we performed a thorough search of numerous databases, including Medline via Ovid, PubMed, Scopus, Web of Science, and Google Scholar. Ten years were searched, encompassing both basic and advanced research. Peer-reviewed papers and original research studies that explicitly looked at the application of AI/ML in endometrial cancer diagnosis were the main targets of the well-defined selection criteria. Using the Critical Appraisal Skills Programme (CASP) methodology, two independent researchers conducted a thorough screening process and quality assessment of included studies. The review found a notable inclination towards the effective use of AI in endometrial carcinoma diagnostics, namely in the identification and categorization of endometrial cancer. Artificial intelligence models, particularly Convolutional Neural Networks (CNNs) and deep learning algorithms have shown remarkable precision in detecting endometrial cancer. They frequently achieve or even exceed the diagnostic proficiency of human specialists. The use of artificial intelligence in medical diagnostics signifies revolutionary progress in the field of oncology. AI-assisted diagnostic tools have demonstrated the potential to improve the precision and effectiveness of cancer diagnosis, namely in cases of endometrial carcinoma. This innovation not only enhances the quality of patient care but also indicates a transition towards more individualized and efficient treatment approaches in the field of oncology. The advancement of AI technology is expected to play a crucial role in medical diagnostics, particularly in the field of cancer detection and treatment, perhaps leading to a significant transformation in the approach to these areas.
摘要：
正确诊断子宫内膜癌对于适当的治疗至关重要。因为这是一个重大的健康风险。随着机器学习（ML）和人工智能（AI）的普及，因此，他们对提高癌症诊断准确性的潜力感兴趣。在子宫内膜癌的背景下，本研究试图检验AI辅助诊断方法的有效性和准确性.此外,旨在有条不紊地评估AI和ML技术对改善子宫内膜癌诊断的贡献.按照PRISMA准则,我们对众多数据库进行了彻底的搜索，包括Medline通过Ovid,PubMed,Scopus,WebofScience,谷歌学者。被搜查了十年，包括基础研究和高级研究。同行评审的论文和原创性研究明确研究了AI/ML在子宫内膜癌诊断中的应用，是明确定义的选择标准的主要目标。使用关键评估技能计划(CASP)方法，两名独立研究人员对纳入的研究进行了全面的筛选和质量评估.该评论发现，在子宫内膜癌诊断中有效使用AI的显着倾向。即子宫内膜癌的鉴定和分类。人工智能模型,特别是卷积神经网络（CNN）和深度学习算法在检测子宫内膜癌方面显示出惊人的精度。他们经常达到甚至超过人类专家的诊断能力。人工智能在医学诊断中的使用标志着肿瘤学领域的革命性进步。人工智能辅助诊断工具已经证明了提高癌症诊断精度和有效性的潜力。即子宫内膜癌的病例。这种创新不仅提高了患者护理的质量，而且表明了肿瘤学领域向更个性化和有效的治疗方法的转变。人工智能技术的进步有望在医疗诊断中发挥关键作用，特别是在癌症检测和治疗领域，也许会导致这些领域的方法发生重大转变。
