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作 者:柏志安[1] 朱铁兵[1] BAI Zhi-an;ZHU Tie-bing(Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine,Shanghai 200025,P.R.C.)
机构地区:[1]上海交通大学医学院附属瑞金医院,上海市黄浦区200025
出 处:《中国数字医学》2020年第7期109-111,69,共4页China Digital Medicine
基 金:国家重点研发计划-数字诊疗装备研发专项(编号:2017YFC0112800)。
摘 要:目的:探索影像人工智能辅助诊断应用于分级诊疗体系中为基层医疗机构提供有益参考,提高基层医疗机构的服务质量与效率。方法:使用大量标注的DR胸片影像作为训练样本,通过深度卷积神经网络的训练,同时在20万例影像数据中做有效迁移学习,自动提取DR胸片中的病灶特征,根据病灶检出状态,对DR胸片结果按照阴性和阳性分类。结果:随机选取上海交通大学附属瑞金医院就诊的504例体检数据,经过数据脱敏处理后进行智能诊断系统应用模拟。利用智能胸片诊断系统,在479例阴性数据中诊断为阴性的为330例,诊断为阳性的为149例。此实验阴性预测准确度为96.77%。结论:使用以深度学习为基础的人工智能诊断模型,可以显著提高基层医疗机构筛查良性胸片的效率和准确率,以及降低医师阅片工作量,有非常广阔的应用前景。Objective:To explore whether the application of image-based artificial intelligence aided diagnosis in the hierarchical diagnosis and treatment system can provide beneficial reference for grass-root medical institutions and improve the service quality and efficiency of them.Methods:A large number of marked DR chest radiographs are used as training samples.Through the training of deep convolutional neural network and effective transfer learning from 200,000 cases of image data,the lesion features in DR chest radiographs are extracted automatically.According to the detection state of the lesions,the results of DR chest radiographs are classified as negative and positive.Results:504 cases of physical examination data of patients who see a doctor in our hospital are randomly selected.After data desensitization,the application simulation of intelligent diagnosis system is made.With the intelligent chest radiograph diagnosis system,in 479 cases of negative data,330 cases are diagnosed as negative and 149 cases are diagnosed as positive.The accuracy of negative prediction in this experiment is 96.77%.Conclusion:The artificial intelligence diagnosis model based on deep learning can significantly improve the efficiency and accuracy of screening out benign chest radiographs in grass-root medical institutions,reduce the workload of doctors of reading films,and have a broad application prospect.
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