利用胸部X光片进行人工智能驱动的COVID-19预后分类：一种深度学习方法
Artificial Intelligence-Driven Prognostic Classification of COVID-19 Using Chest X-rays: A Deep Learning Approach
AI总结：
本文基于AIforCOVID的1,103张胸部X光图像，在Microsoft Azure Custom Vision上构建CNN模型，对轻、中、重度COVID-19进行预后分类，平均准确率达97%。
摘要：
背景：COVID-19大流行已使医疗系统不堪重负，凸显出需要人工智能驱动的工具来辅助快速、准确的患者预后判断。胸部X光成像是一种广泛可用的诊断工具，但现有预后分类方法缺乏可扩展性和效率。目的：本研究提出一种高精度深度学习模型，利用胸部X光图像对COVID-19严重程度（轻度、中度和重度）进行分类，该模型在Microsoft Azure Custom Vision上开发。方法：使用来自AIforCOVID的包含1,103张经确诊COVID-19X光图像的数据集，我们训练并验证了一个利用卷积神经网络（CNNs）的深度学习模型。该模型在一个未见过的数据集上进行评估，以测量准确率、精确率和召回率。结果：我们的模型取得了97%的平均准确率，特异度为99%，灵敏度为87%，F1分数为93.11%。在对COVID-19严重程度进行分类时，该模型取得的准确率分别为89.03%（轻度）、95.77%（中度）和81.16%（重度）。这些结果证明了该模型在真实临床应用中的潜力，有助于更快决策并改善资源分配。结论：利用深度学习进行人工智能驱动的预后分类可显著增强COVID-19患者管理，实现早期干预和高效分诊。本研究提供了一个可扩展、高精度的AI框架，用于将深度学习整合到常规临床工作流程中。未来工作应聚焦于扩展数据集、外部验证和监管合规，以促进临床采用。
英文摘要：
Background: The COVID-19 pandemic has overwhelmed healthcare systems, emphasizing the need for AI-driven tools to assist in rapid and accurate patient prognosis. Chest X-ray imaging is a widely available diagnostic tool, but existing methods for prognosis classification lack scalability and efficiency. Objective: This study presents a high-accuracy deep learning model for classifying COVID-19 severity (Mild, Moderate, and Severe) using Chest X-ray images, developed on Microsoft Azure Custom Vision. Methods: Using a dataset of 1,103 confirmed COVID-19 X-ray images from AIforCOVID, we trained and validated a deep learning model leveraging Convolutional Neural Networks (CNNs). The model was evaluated on an unseen dataset to measure accuracy, precision, and recall. Results: Our model achieved an average accuracy of 97%, with specificity of 99%, sensitivity of 87%, and an F1-score of 93.11%. When classifying COVID-19 severity, the model achieved accuracies of 89.03% (Mild), 95.77% (Moderate), and 81.16% (Severe). These results demonstrate the model's potential for real-world clinical applications, aiding in faster decision-making and improved resource allocation. Conclusion: AI-driven prognosis classification using deep learning can significantly enhance COVID-19 patient management, enabling early intervention and efficient triaging. Our study provides a scalable, high-accuracy AI framework for integrating deep learning into routine clinical workflows. Future work should focus on expanding datasets, external validation, and regulatory compliance to facilitate clinical adoption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
