用于胸部X光多病理检测的自主AI：印度医疗保健系统中的多中心研究
Autonomous AI for Multi-Pathology Detection in Chest X-Rays: A Multi-Site Study in the Indian Healthcare System
AI总结：
本研究开发了一种基于多模型的自主AI系统，在印度多中心500万张X光片上训练，实现对75种胸部病理的精准分类、检测与分割，在17家机构部署处理超15万次扫描，显著提升了诊断效率与准确性。
摘要：
研究设计：本研究概述了一种用于胸部X光（CXR）解读的自主AI系统的开发，该系统在来自印度各地医疗保健系统的超过500万张X光片的大型数据集上进行了训练。该AI系统集成了包括Vision Transformers、Faster R-CNN和各种U-Net模型（如Attention U-Net、U-Net++和Dense U-Net）在内的先进架构，以实现对75种不同病理的全面分类、检测和分割。为确保鲁棒性，研究设计包括按年龄、性别和设备类型进行的亚组分析，验证了模型在不同患者人口统计学特征和成像环境中的适应性和性能。性能：该AI系统在多病理分类方面实现了高达98%的精确率和超过95%的召回率，并且在人口统计学和设备亚组中表现稳定。对于正常与异常分类，它达到了99.8%的精确率、99.6%的召回率和99.9%的阴性预测值（NPV）。它被部署在印度的17个主要医疗保健系统中，包括诊断中心、大型医院和政府医院。在部署期间，该系统处理了超过150,000次扫描，平均每天处理2,000张胸部X光片，从而缩短了报告时间并提高了诊断准确性。结论：高精确率和召回率验证了该AI作为自主正常异常分类、病理定位和分割的可靠工具的能力。这种可扩展的AI模型解决了服务不足地区的诊断差距，优化了放射学工作流程，并提升了印度不同医疗保健环境中的患者护理水平。
英文摘要：
Study Design: The study outlines the development of an autonomous AI system for chest X-ray (CXR) interpretation, trained on a vast dataset of over 5 million X rays sourced from healthcare systems across India. This AI system integrates advanced architectures including Vision Transformers, Faster R-CNN, and various U Net models (such as Attention U-Net, U-Net++, and Dense U-Net) to enable comprehensive classification, detection, and segmentation of 75 distinct pathologies. To ensure robustness, the study design includes subgroup analyses across age, gender, and equipment type, validating the model's adaptability and performance across diverse patient demographics and imaging environments. Performance: The AI system achieved up to 98% precision and over 95% recall for multi pathology classification, with stable performance across demographic and equipment subgroups. For normal vs. abnormal classification, it reached 99.8% precision, 99.6% recall, and 99.9% negative predictive value (NPV). It was deployed in 17 major healthcare systems in India including diagnostic centers, large hospitals, and government hospitals. Over the deployment period, the system processed over 150,000 scans, averaging 2,000 chest X rays daily, resulting in reduced reporting times and improved diagnostic accuracy. Conclusion: The high precision and recall validate the AI's capability as a reliable tool for autonomous normal abnormal classification, pathology localization, and segmentation. This scalable AI model addresses diagnostic gaps in underserved areas, optimizing radiology workflows and enhancing patient care across diverse healthcare settings in India.
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。
