综述AI在贫血诊断中的应用，对比多种模型表现。
AI-Driven anemia diagnosis: A review of advanced models and techniques
- 系统梳理机器学习与深度学习在贫血检测中的应用方法
- 对比模型性能，准确率、敏感性等指标差异显著
- 适合医疗AI研究者与临床诊断优化参考
贫血是一种红细胞或血红蛋白水平不足的常见健康问题，影响全球数百万人。及时准确的诊断对有效管理至关重要。近年来，人工智能技术（如机器学习和深度学习）在贫血检测、分类和诊断中受到广泛关注。本文系统回顾了该领域的最新进展，重点分析多种应用于贫血检测的模型，并基于准确率、灵敏度、特异性和精确率等性能指标进行比较。通过评估这些指标，论文揭示了各模型在贫血识别与分类中的优缺点，强调需解决相关因素以提升诊断准确性。
原文摘要 · Abstract (English)
Anemia, a condition marked by insufficient levels of red blood cells or hemoglobin, remains a widespread health issue affecting millions of individuals globally. Accurate and timely diagnosis is essential for effective management and treatment of anemia. In recent years, there has been a growing interest in the use of artificial intelligence techniques, i.e., machine learning (ML) and deep learning (DL) for the detection, classification, and diagnosis of anemia. This paper provides a systematic review of the recent advancements in this field, with a focus on various models applied to anemia detection. The review also compares these models based on several performance metrics, including accuracy, sensitivity, specificity, and precision. By analyzing these metrics, the paper evaluates the strengths and limitation of discussed models in detecting and classifying anemia, emphasizing the importance of addressing these factors to improve diagnostic accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。