Ecotourism requires the harmony of all factors involved in tourism system for a multilateral benefit. Based on such cognition, a concept of deep ecotourism development is put forward which includes two connotations: o...Ecotourism requires the harmony of all factors involved in tourism system for a multilateral benefit. Based on such cognition, a concept of deep ecotourism development is put forward which includes two connotations: on the one hand, it should give prominence to the display of the eco-culture of the tourist destination and tourists' eco-experience, in which way the development behavior on the tourist destination and the tourists' behavior will be regulated; on the other hand, it implies the deep harmony among tourist entrepreneurs and tourists, the local governments and the local residents, as well as tourist activities and the ecological environment in the tourism development for the multilateral benefit of every element involved and sustainable tourism development. The common sense is that the degrees in a certain tourism destination will differ and that consequently four levels of ecotourism are divided - very shallow ecotourism, shallow ecotourism, deep ecotourism and very deep ecotourism. To move shallow ecotourism toward deep one, two models of "four subjects and two wings" and "connecting the two wings" of deep ecotourism development system are introduced to make ecotourism industry favorable to the display of eco-culture and the sustainable development of the destination community. With the two models, a case study of ecotourism development in Louguantai National Forest Park was made as a demonstration. The ultimate purpose is to build an ideal new Shangri-La.展开更多
In recent years,Henan sticks to the points of General Secretary Xi Jinping,makes full use of its rich resources and relations,makes efforts to deepen friendship with people around the world,promotes relations with for...In recent years,Henan sticks to the points of General Secretary Xi Jinping,makes full use of its rich resources and relations,makes efforts to deepen friendship with people around the world,promotes relations with foreign countries,pushes展开更多
目的开发一种深度学习系统用于成人发育性髋关节发育不良(Developmental dysplasia of the hip,DDH)患者的Crowe分型辅助诊断,并且分析该系统对于帮助临床医学生掌握DDH分型的可行性。方法纳入149例X线片训练集、42例测试集以及21例验证...目的开发一种深度学习系统用于成人发育性髋关节发育不良(Developmental dysplasia of the hip,DDH)患者的Crowe分型辅助诊断,并且分析该系统对于帮助临床医学生掌握DDH分型的可行性。方法纳入149例X线片训练集、42例测试集以及21例验证集,分割盆骨、提取DDH局部图像块,将金标准结果与医学生、AI辅助医学生评估结果进行比较。结果测试集共纳入42例,其中女性30例,男性12例,年龄(69±12)岁,涉及发育不良髋关节67侧(左30侧,右37侧)。AI、医学生、AI辅助医学生评估结果与金标准的相关性为0.906[95%CI(0.850,0.941)]、0.823[95%CI(0.726,0.887)]、0.886[95%CI(0.821,0.929)];准确率分别为0.87、0.78、0.88;精确度分别为0.88、0.83、0.89;召回率分别为0.87、0.78、0.88;F1值分别为0.87、0.80、0.88。混淆矩阵和条件概率结果显示,预测准确率Ⅰ型DDH三组分别为0.98、0.88、0.96,Ⅱ型DDH三组分别为0.40、0.20、0.40,Ⅲ型DDH三组分别为0.56、0.67、0.78;Ⅳ型DDH三组分别为0.88、0.75、0.88。结论深度学习辅助诊断系统可以有效提高医学生对于DDH分型的评估能力,可作为医学生学习掌握DDH影像诊断的培训工具。展开更多
使用骨盆X光片诊断发育性髋关节发育不良(Developmental Dysplasia of the Hip,DDH)要求准确地标注髋关节关键点,而深度学习方法能作为可靠的辅助工具。针对骨盆片拍摄姿势和拍摄距离多样化问题,本文基于U-Net提出了RKD-UNet来检测髋关...使用骨盆X光片诊断发育性髋关节发育不良(Developmental Dysplasia of the Hip,DDH)要求准确地标注髋关节关键点,而深度学习方法能作为可靠的辅助工具。针对骨盆片拍摄姿势和拍摄距离多样化问题,本文基于U-Net提出了RKD-UNet来检测髋关节关键点。该模型使用残差块改进U-Net的卷积层和skip-connection路径,并将坐标注意力引入到编码器中以增强模型对关键点邻域的特征提取能力。在编码器顶部使用卷积和ASPP模块构成Bridge块,以[3,6,9]的空洞率融合不同尺度的特征信息并提升模型的感受野。本文使用包含骨盆正位片、蛙位片、下肢全长片和术后骨盆片的数据集训练和测试模型。RKD-UNet实现了3.19±2.19 px的平均关键点检测误差和2.83°±2.59°的平均髋臼角测量误差。对正常、轻度、中度和重度脱位案例诊断的F1分数分别达到89.6、77.1、57.9和94.1,高于医生的手动诊断结果。实验结果表明,RKD-UNet能准确检测髋关节关键点并辅助医生诊断DDH。展开更多
The Bashu Cultural and Tourism Corridor is a region centered on Chongqing's main city and Chengdu,with the high-speed railways,expressways,and cities(counties)along the Yangtze River system connecting the two plac...The Bashu Cultural and Tourism Corridor is a region centered on Chongqing's main city and Chengdu,with the high-speed railways,expressways,and cities(counties)along the Yangtze River system connecting the two places as important components.Sichuan and Chongqing,guided by the"Bashu Cultural and Tourism Corridor Construction Plan,"have issued a series of policies and systems for the development of Bashu cultural tourism,vigorously promoting the construction of the Bashu Cultural and Tourism Corridor.However,in the process of promoting the construction of the Bashu Cultural and Tourism Corridor,there are also many challenges,such as insufficient integration of the cultural and tourism industry,outdated development models for cultural tourism,imperfect coordination mechanisms within the industry,a large gap in professional talents in the cultural and tourism industry,and inadequate exploration of characteristic cultural tourism resources.Among these,the insufficient integration of the cultural and tourism industry is particularly prominent,manifested in the disappearance of regional culture,a significant loss of talent,and low-quality development of the tourism industry.Cultural and tourism integration is a deep-level integration in the fields of concepts,functions,resources,industries,and technologies.This topic focuses on the issue of deep integration of cultural and tourism development in the construction of the Bashu Cultural and Tourism Corridor,which has important theoretical and practical significance.展开更多
目的探讨人工智能(artificial intelligence,AI)模型在骨盆X线片上测量髋关节关键角的准确性,并评价AI模型对成人发育性髋关节发育不良(developmental dysplasia of the hip,DDH)和临界型DDH(borderline developmental dysplasia of the...目的探讨人工智能(artificial intelligence,AI)模型在骨盆X线片上测量髋关节关键角的准确性,并评价AI模型对成人发育性髋关节发育不良(developmental dysplasia of the hip,DDH)和临界型DDH(borderline developmental dysplasia of the hip,BDDH)的诊断效能。方法回顾性分析来源于解放军总医院第四医学中心放射科1029例可疑DDH患者的病历资料,男273例、女756例,年龄(57.01±18.16)岁(范围12~88岁)。随机分配到训练集720例、测试集206例和验证集103例。由两名放射科医生在骨盆正位X线片上确定并标记髋关节关键解剖点,应用训练集进行深度学习,构建定位髋关节关键解剖点的AI模型,AI模型基于髋关节关键解剖点自动测量并计算Sharp角、Tönnis角和中心边缘(center-edge angle,CE)角。将放射科医生测量结果与AI模型自动测量结果进行比较,用于评估AI模型对测试集测量结果的准确性。验证集用于优化模型参数,测试集用于评估DDH的诊断性能。绘制受试者工作特征(receiver operating characteristic curve,ROC)曲线,计算ROC曲线下面积(area under roc curve,AUC)评价AI模型对DDH和BDDH的诊断效能。结果AI模型测量髋关节左侧Sharp角、Tönnis角及CE角诊断DDH的准确率分别为89.8%、86.8%、90.1%,右侧的准确率为93.7%、80.5%、92.2%,人工测量平均值和AI模型测量的Sharp角、Tönnis角和CE角的差异均无统计学意义(P>0.05)。AI模型与人工测量Sharp角、Tönnis角和CE角的相关性检验及一致性分析结果显示r值及组内相关系数(intraclass correlation coefficient,ICC)均>0.75。AI模型测量用时(1.7±0.1)s,较放射科医生人工测量用时的(88.1±8.4)s和(90.3±7.4)s更短(P<0.05)。AI模型测量得到的Sharp角、Tönnis角、CE角诊断DDH的AUC分别为0.883、0.908、0.922(左侧)和0.924、0.922、0.871(右侧);AI模型测量左、右侧CE角诊断BDDH的AUC分别为0.787和0.676。AI模型和人工测量诊断DDH和BDDH与临床最终诊断结果一致性的Kappa检验结果显示,CE角(AI模型C)的κ=0.663,而Sharp角AI模型、Tönnis角AI模型及人工测量结果诊断的κ值均>0.800。结论基于深度学习的卷积神经网络AI模型可以实现自动测量Sharp角、Tönnis角和CE角,辅助诊断DDH和BDDH具有较高的诊断效能。展开更多
文摘Ecotourism requires the harmony of all factors involved in tourism system for a multilateral benefit. Based on such cognition, a concept of deep ecotourism development is put forward which includes two connotations: on the one hand, it should give prominence to the display of the eco-culture of the tourist destination and tourists' eco-experience, in which way the development behavior on the tourist destination and the tourists' behavior will be regulated; on the other hand, it implies the deep harmony among tourist entrepreneurs and tourists, the local governments and the local residents, as well as tourist activities and the ecological environment in the tourism development for the multilateral benefit of every element involved and sustainable tourism development. The common sense is that the degrees in a certain tourism destination will differ and that consequently four levels of ecotourism are divided - very shallow ecotourism, shallow ecotourism, deep ecotourism and very deep ecotourism. To move shallow ecotourism toward deep one, two models of "four subjects and two wings" and "connecting the two wings" of deep ecotourism development system are introduced to make ecotourism industry favorable to the display of eco-culture and the sustainable development of the destination community. With the two models, a case study of ecotourism development in Louguantai National Forest Park was made as a demonstration. The ultimate purpose is to build an ideal new Shangri-La.
文摘In recent years,Henan sticks to the points of General Secretary Xi Jinping,makes full use of its rich resources and relations,makes efforts to deepen friendship with people around the world,promotes relations with foreign countries,pushes
文摘目的开发一种深度学习系统用于成人发育性髋关节发育不良(Developmental dysplasia of the hip,DDH)患者的Crowe分型辅助诊断,并且分析该系统对于帮助临床医学生掌握DDH分型的可行性。方法纳入149例X线片训练集、42例测试集以及21例验证集,分割盆骨、提取DDH局部图像块,将金标准结果与医学生、AI辅助医学生评估结果进行比较。结果测试集共纳入42例,其中女性30例,男性12例,年龄(69±12)岁,涉及发育不良髋关节67侧(左30侧,右37侧)。AI、医学生、AI辅助医学生评估结果与金标准的相关性为0.906[95%CI(0.850,0.941)]、0.823[95%CI(0.726,0.887)]、0.886[95%CI(0.821,0.929)];准确率分别为0.87、0.78、0.88;精确度分别为0.88、0.83、0.89;召回率分别为0.87、0.78、0.88;F1值分别为0.87、0.80、0.88。混淆矩阵和条件概率结果显示,预测准确率Ⅰ型DDH三组分别为0.98、0.88、0.96,Ⅱ型DDH三组分别为0.40、0.20、0.40,Ⅲ型DDH三组分别为0.56、0.67、0.78;Ⅳ型DDH三组分别为0.88、0.75、0.88。结论深度学习辅助诊断系统可以有效提高医学生对于DDH分型的评估能力,可作为医学生学习掌握DDH影像诊断的培训工具。
文摘使用骨盆X光片诊断发育性髋关节发育不良(Developmental Dysplasia of the Hip,DDH)要求准确地标注髋关节关键点,而深度学习方法能作为可靠的辅助工具。针对骨盆片拍摄姿势和拍摄距离多样化问题,本文基于U-Net提出了RKD-UNet来检测髋关节关键点。该模型使用残差块改进U-Net的卷积层和skip-connection路径,并将坐标注意力引入到编码器中以增强模型对关键点邻域的特征提取能力。在编码器顶部使用卷积和ASPP模块构成Bridge块,以[3,6,9]的空洞率融合不同尺度的特征信息并提升模型的感受野。本文使用包含骨盆正位片、蛙位片、下肢全长片和术后骨盆片的数据集训练和测试模型。RKD-UNet实现了3.19±2.19 px的平均关键点检测误差和2.83°±2.59°的平均髋臼角测量误差。对正常、轻度、中度和重度脱位案例诊断的F1分数分别达到89.6、77.1、57.9和94.1,高于医生的手动诊断结果。实验结果表明,RKD-UNet能准确检测髋关节关键点并辅助医生诊断DDH。
基金sponsored by Chengdu department-level project“Research on the deep integration and development of Chinese tourism in the construction of the Bashu Cultural Tourism Corridor”.Number:CYSC23C008.
文摘The Bashu Cultural and Tourism Corridor is a region centered on Chongqing's main city and Chengdu,with the high-speed railways,expressways,and cities(counties)along the Yangtze River system connecting the two places as important components.Sichuan and Chongqing,guided by the"Bashu Cultural and Tourism Corridor Construction Plan,"have issued a series of policies and systems for the development of Bashu cultural tourism,vigorously promoting the construction of the Bashu Cultural and Tourism Corridor.However,in the process of promoting the construction of the Bashu Cultural and Tourism Corridor,there are also many challenges,such as insufficient integration of the cultural and tourism industry,outdated development models for cultural tourism,imperfect coordination mechanisms within the industry,a large gap in professional talents in the cultural and tourism industry,and inadequate exploration of characteristic cultural tourism resources.Among these,the insufficient integration of the cultural and tourism industry is particularly prominent,manifested in the disappearance of regional culture,a significant loss of talent,and low-quality development of the tourism industry.Cultural and tourism integration is a deep-level integration in the fields of concepts,functions,resources,industries,and technologies.This topic focuses on the issue of deep integration of cultural and tourism development in the construction of the Bashu Cultural and Tourism Corridor,which has important theoretical and practical significance.
文摘目的探讨人工智能(artificial intelligence,AI)模型在骨盆X线片上测量髋关节关键角的准确性,并评价AI模型对成人发育性髋关节发育不良(developmental dysplasia of the hip,DDH)和临界型DDH(borderline developmental dysplasia of the hip,BDDH)的诊断效能。方法回顾性分析来源于解放军总医院第四医学中心放射科1029例可疑DDH患者的病历资料,男273例、女756例,年龄(57.01±18.16)岁(范围12~88岁)。随机分配到训练集720例、测试集206例和验证集103例。由两名放射科医生在骨盆正位X线片上确定并标记髋关节关键解剖点,应用训练集进行深度学习,构建定位髋关节关键解剖点的AI模型,AI模型基于髋关节关键解剖点自动测量并计算Sharp角、Tönnis角和中心边缘(center-edge angle,CE)角。将放射科医生测量结果与AI模型自动测量结果进行比较,用于评估AI模型对测试集测量结果的准确性。验证集用于优化模型参数,测试集用于评估DDH的诊断性能。绘制受试者工作特征(receiver operating characteristic curve,ROC)曲线,计算ROC曲线下面积(area under roc curve,AUC)评价AI模型对DDH和BDDH的诊断效能。结果AI模型测量髋关节左侧Sharp角、Tönnis角及CE角诊断DDH的准确率分别为89.8%、86.8%、90.1%,右侧的准确率为93.7%、80.5%、92.2%,人工测量平均值和AI模型测量的Sharp角、Tönnis角和CE角的差异均无统计学意义(P>0.05)。AI模型与人工测量Sharp角、Tönnis角和CE角的相关性检验及一致性分析结果显示r值及组内相关系数(intraclass correlation coefficient,ICC)均>0.75。AI模型测量用时(1.7±0.1)s,较放射科医生人工测量用时的(88.1±8.4)s和(90.3±7.4)s更短(P<0.05)。AI模型测量得到的Sharp角、Tönnis角、CE角诊断DDH的AUC分别为0.883、0.908、0.922(左侧)和0.924、0.922、0.871(右侧);AI模型测量左、右侧CE角诊断BDDH的AUC分别为0.787和0.676。AI模型和人工测量诊断DDH和BDDH与临床最终诊断结果一致性的Kappa检验结果显示,CE角(AI模型C)的κ=0.663,而Sharp角AI模型、Tönnis角AI模型及人工测量结果诊断的κ值均>0.800。结论基于深度学习的卷积神经网络AI模型可以实现自动测量Sharp角、Tönnis角和CE角,辅助诊断DDH和BDDH具有较高的诊断效能。
