用AI分析胸片预测新冠病情严重程度，准确率达97%。
Artificial Intelligence-Driven Prognostic Classification of COVID-19 Using Chest X-rays: A Deep Learning Approach
- 基于卷积神经网络的深度学习模型，从胸片中识别病情
- 整体准确率97%，对轻症、中症、重症分别达89%、96%、81%
- 适合临床快速分诊，助力医疗资源合理分配
背景：新冠疫情冲击全球医疗系统，亟需AI工具辅助快速精准预后判断。胸片是广泛应用的诊断手段，但现有预后分类方法缺乏可扩展性和效率。目标：本研究提出一种高精度深度学习模型，利用微软Azure定制视觉平台，基于1,103张确诊新冠患者胸片（来自AIforCOVID数据集）训练，实现对病情严重程度（轻度、中度、重度）的分类。方法：采用卷积神经网络（CNN）进行训练与验证，并在未见数据集上评估准确率、精确率和召回率。结果：模型平均准确率达97%，特异性99%，敏感性87%，F1分数93.11%。在具体分类中，轻症准确率为89.03%，中症为95.77%，重症为81.16%。结果表明该模型具备实际临床应用潜力，可加速决策并优化资源配置。结论：基于深度学习的AI预后分类能显著提升新冠患者管理效率，支持早期干预与高效分诊。本研究提供了一个可扩展、高精度的AI框架，便于融入常规临床流程。未来工作应聚焦数据集扩充、外部验证及合规性，推动临床落地。
原文摘要 · Abstract (English)
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.
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