背景:目前用于全膝关节置换的机器人系统设计的基本原理是将三维手术规划、术中危险区预警、实时数据反馈及机械臂辅助截骨等技术相结合,以实现全膝关节置换的精准化、个性化,这也恰好是它最大优势所在,因此近年来成为关节外科领域热点...背景:目前用于全膝关节置换的机器人系统设计的基本原理是将三维手术规划、术中危险区预警、实时数据反馈及机械臂辅助截骨等技术相结合,以实现全膝关节置换的精准化、个性化,这也恰好是它最大优势所在,因此近年来成为关节外科领域热点话题,备受关注。目的:文章将从机器人辅助全膝关节置换在关节外科领域的发展现状及其与传统全膝关节置换优劣势对比进行概述,此外,还将对机器人辅助全膝关节置换技术未来的发展进行展望。方法:应用计算机检索PubMed、中国知网、万方和维普数据库的相关文章,英文检索词:“robot OR robotic OR robotics OR robotically OR computer,total knee arthroplasty OR total knee replacement,TKA OR TKR”,中文检索词:“机器人辅助,计算机导航,全膝关节置换术”,最终纳入64篇文献进行综述分析。结果与结论:①用于辅助全膝关节置换的机器人系统根据其自由度分为主动式、半主动式和被动式。半主动式系统是目前使用最为广泛的机器人系统,该系统有效提高了全膝关节置换手术的精准性和个性化程度,但其高昂的使用成本与较长的学习曲线仍是在该领域内推广时需要权衡的主要因素。②机器人辅助全膝关节置换可实现膝关节局部三维空间的精准截骨、正确安置假体,已被广泛证明可以提供更好的假体植入精准度,减少影像学异常值,在术中可获得良好的软组织平衡,最终改善术后膝关节运动及功能状态。③但目前的机器人辅助系统依然存在客观的不足之处,包括不同机器人设备与术者之间的学习曲线问题、额外增加的安装和维护成本以及与机器人手术相关的潜在并发症,所以其能否让医疗系统及患者真正受益仍需要更长期的研究予以证明,机器人辅助系统也仍需进行更多实质性的改进。④机器人辅助全膝关节置换技术在临床上仍然处于初步研究阶段,并没有大范围地应用到临床,更加明确该技术的用法、完善该技术的临床操作规范和安全性成为了未来对该技术的研究侧重点。展开更多
Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and...Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.展开更多
文摘背景:目前用于全膝关节置换的机器人系统设计的基本原理是将三维手术规划、术中危险区预警、实时数据反馈及机械臂辅助截骨等技术相结合,以实现全膝关节置换的精准化、个性化,这也恰好是它最大优势所在,因此近年来成为关节外科领域热点话题,备受关注。目的:文章将从机器人辅助全膝关节置换在关节外科领域的发展现状及其与传统全膝关节置换优劣势对比进行概述,此外,还将对机器人辅助全膝关节置换技术未来的发展进行展望。方法:应用计算机检索PubMed、中国知网、万方和维普数据库的相关文章,英文检索词:“robot OR robotic OR robotics OR robotically OR computer,total knee arthroplasty OR total knee replacement,TKA OR TKR”,中文检索词:“机器人辅助,计算机导航,全膝关节置换术”,最终纳入64篇文献进行综述分析。结果与结论:①用于辅助全膝关节置换的机器人系统根据其自由度分为主动式、半主动式和被动式。半主动式系统是目前使用最为广泛的机器人系统,该系统有效提高了全膝关节置换手术的精准性和个性化程度,但其高昂的使用成本与较长的学习曲线仍是在该领域内推广时需要权衡的主要因素。②机器人辅助全膝关节置换可实现膝关节局部三维空间的精准截骨、正确安置假体,已被广泛证明可以提供更好的假体植入精准度,减少影像学异常值,在术中可获得良好的软组织平衡,最终改善术后膝关节运动及功能状态。③但目前的机器人辅助系统依然存在客观的不足之处,包括不同机器人设备与术者之间的学习曲线问题、额外增加的安装和维护成本以及与机器人手术相关的潜在并发症,所以其能否让医疗系统及患者真正受益仍需要更长期的研究予以证明,机器人辅助系统也仍需进行更多实质性的改进。④机器人辅助全膝关节置换技术在临床上仍然处于初步研究阶段,并没有大范围地应用到临床,更加明确该技术的用法、完善该技术的临床操作规范和安全性成为了未来对该技术的研究侧重点。
基金National Natural Science Foundation of China(Grant number:11904327,61905223,and 62073299)Training Plan of Young Backbone Teachers in Universities of Henan Province(2023GGJS087)+1 种基金Henan Provincial Science and Technology Research Project(222102110279,222102210085,and 242102210157)Project of Central Plains Science and Technology Innovation Leading Talents(224200510026).
文摘Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.
