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作 者:杨聚加 段然 吴亚光 冉杰 朱浩 YANG Jujia;DUAN Ran;WU Yaguang;RAN Jie;ZHU Hao(Department of Information,the First Affiliated Hospital of Army Medical University,Chongqing 400038,China;Department of Information,Chongqing People′s Hospital,Chongqing 400013,China;Department of Dermatology,the First Affiliated Hospital of Army Medical University,Chongqing 400038,China;School of Automation,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
机构地区:[1]陆军军医大学第一附属医院信息科,重庆400038 [2]重庆市人民医院信息处,400013 [3]陆军军医大学第一附属医院皮肤科,重庆400038 [4]重庆邮电大学自动化学院,400065
出 处:《重庆医学》2022年第3期507-511,共5页Chongqing medicine
基 金:重庆市科卫联合医学科研项目面上项目(2019MSXM093)。
摘 要:目的建立一种基于数据分类算法、人工智能与皮肤诊断学等多学科融合的寻常型痤疮智慧诊疗模型。方法通过采集陆军军医大学第一附属医院皮肤科2018-2020年门诊患者中明确确诊的寻常型痤疮皮肤影像数据为依据,利用数据分类算法、卷积神经网络、深度学习等技术定位影像数据中的痤疮关键部位,进而提取、处理、分析和总结归纳寻常型痤疮影像的病理特征。结果建立痤疮分类诊断模型与知识库,构建智慧痤疮诊断模型标准与规范。结论数据分类算法智慧诊疗模型可实现对寻常型痤疮图像的自主诊断,提高诊断准确率与工作效率。Objective To establish an intelligent diagnosis and treatment model for common acne based on a multidisciplinary integration of data classification algorithms,artificial intelligence and skin diagnostics.Methods The skin image data of acne vulgaris clearly diagnosed in outpatients from 2018 to 2020 in the department of dermatology of the First Affiliated Hospital of Army Military Medical University were collected as the basis.Data classification algorithms,convolutional neural networks and deep learning were used to locate key acne areas in the image data.In turn,pathological features of acne vulgaris images were extracted,processed,analyzed and summarized.Results An acne classification and diagnosis model and knowledge base were established,and standards and specifications for smart acne diagnosis models were constructed.Conclusion The data classification algorithm smart diagnosis model can achieve an autonomous diagnosis of common acne images and improve diagnostic accuracy and efficiency.
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