A considerable portion of the population now experiences osteoarthritis of the knee,spine,and hip due to lifestyle changes.Therefore,early treatment,recognition and prevention are essential to reduce damage;neverthele...A considerable portion of the population now experiences osteoarthritis of the knee,spine,and hip due to lifestyle changes.Therefore,early treatment,recognition and prevention are essential to reduce damage;nevertheless,this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians.To overcome the existing challenges in the early detection of Knee Osteoarthritis(KOA),an effective automated technique,prompt recognition,and correct categorization are required.This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence(KL) classification schemes,such as Class-I,Class-II,Class-III,and Class-IV.Utilizing ResNet to segregate knee pictures,we first collected features from these images before using the Bidirectional Long Short-Term Memory(BiLSTM)architecture to classify them.Given that the technique is a pre-trained network and doesn’t require a large training set,the Mendeley VI dataset has been utilized for the training of the proposed model.To evaluate the effectiveness of the suggested model,cross-validation has also been employed using the Osteoarthritis Initiative(OAI)dataset.Furthermore,our suggested technique is more resilient,which overcomes the challenge of imbalanced training data due to the hybrid architecture of our proposed model.The suggested algorithm is a cuttingedge and successful method for documenting the successful application of the timely identification and severity categorization of KOA.The algorithm showed a cross-validation accuracy of 78.57%and a testing accuracy of 84.09%.Numerous tests have been conducted to show that our suggested algorithm is more reliable and capable than the state-of-the-art at identifying and categorizing KOA disease.展开更多
目的:探讨水解南珠液对兔膝骨关节炎相关症状、软骨羟脯氨酸和糖醛酸的影响。方法:将新西兰白兔40只随机分为4组,分别为水解南珠液高剂量组、低剂量组、模型组、正常组。除正常组外,其余兔右膝关节腔内注射0.5 ml 4%木瓜蛋白酶造模,分...目的:探讨水解南珠液对兔膝骨关节炎相关症状、软骨羟脯氨酸和糖醛酸的影响。方法:将新西兰白兔40只随机分为4组,分别为水解南珠液高剂量组、低剂量组、模型组、正常组。除正常组外,其余兔右膝关节腔内注射0.5 ml 4%木瓜蛋白酶造模,分别予以灭菌水解南珠液高剂量0.2 ml、低剂量0.1 ml,模型组0.1 ml生理盐水,连续隔日患肢关节腔注射7次共14日。测定各实验组膝骨性关节炎严重性指数(Lequesne评分)、软骨羟脯氨酸和糖醛酸含量。结果:造模结束后,模型出现明显膝骨关节炎症状。干预第7日、14日后,高、低剂量组与模型组的Lequesne评分比较有统计学意义(P<0.05),高、低剂量组与模型组软骨羟脯氨酸和糖醛酸含量比较有统计学意义(P<0.05),高剂量和低剂量比较无统计学意义(P>0.05)。结论:水解南珠液能改善实验动物膝骨关节炎相关症状和软骨羟脯氨酸和糖醛酸含量,且高低剂量效果无差异。展开更多
膝骨性关节炎（KOA ）又称膝骨关节病、骨关节炎、退行性关节炎、或增生性骨关节炎骨关节病。KOA的主要病理改变为软骨退行性变性和消失，以及关节边缘韧带附着处和软骨下骨质反应性增生形成骨赘，病变常累及整个关节和周围组织并由此...膝骨性关节炎（KOA ）又称膝骨关节病、骨关节炎、退行性关节炎、或增生性骨关节炎骨关节病。KOA的主要病理改变为软骨退行性变性和消失，以及关节边缘韧带附着处和软骨下骨质反应性增生形成骨赘，病变常累及整个关节和周围组织并由此引起关节疼痛、僵直、畸形和关节障碍。该病是常见的一种关节病变，发病率约占整个骨关节炎（O A ）发病率的80％，K O A发病与很多因素有关，如年龄、体重、骨密度、创伤、内分泌等。其发病率随着年龄而增加，女性比男性多发。国外调查指出，有明显骨关节炎X线片证据者，在45～64岁年龄组中，男性占25％，女性占30％；而在65岁以上年龄组中，男性上升为58％，女性上升为65％。通过临床调查也证实，骨关节炎的发病率在59～69岁之间占29％，而在75岁或以上的占70％。随着人口老龄化程度的加深，这一问题将会愈加严重。1999年世界卫生组织把骨性关节炎与心血管疾病及癌症列为危害人类健康的三大杀手，将2000-2010年定为“骨与关节十年”，并将每年的10月12日定为“国际关节炎日”［1］。探索有效的治疗方法，是临床医疗工作者一直追求的目标，也是本课题的最终目的。展开更多
目的：探究中期KOA患者痛点规律及在玻璃酸钠膝关节注射基础上对比柴胡桂枝汤与地塞米松磷酸钠合利多卡因对中期KOA患者痛点镇痛效果。方法：2012年3月—2015年3月就诊于云南省中医医院和昆明市中医医院骨科符合KOA诊断标准及纳入标准患...目的：探究中期KOA患者痛点规律及在玻璃酸钠膝关节注射基础上对比柴胡桂枝汤与地塞米松磷酸钠合利多卡因对中期KOA患者痛点镇痛效果。方法：2012年3月—2015年3月就诊于云南省中医医院和昆明市中医医院骨科符合KOA诊断标准及纳入标准患者154例,随机分为对照组（77例、91膝）和研究组（77例、93膝）。2组患者均予玻璃酸钠注射液膝关节腔注射1次/周,连用4周;对照组加用地塞米松磷酸钠注射液合利多卡因注射液于第1周及第4周（必要时）;研究组口服柴胡桂枝汤100 m L,tid,连用1周间隔1周后再用1周,共用2周。4周后对比2组治疗前后痛点VAS疼痛评分（9分法）及临床疗效。结果：研究组有效率（67.1%）高于对照组（58.3%）,且研究组（27.1%）治疗期间止痛药应用情况低于对照组（41.6%）;按照膝关节9分法分区,中期KOA痛点多集中于外侧,治疗前各区痛点VAS评分无差异（P〉0.05）,治疗后研究组外侧区及全膝痛点VAS评分低于对照组（P〈0.05）。结论：KOA中期的基本病机可能在于＂枢机不利、气血痹阻＂,同时可能与膝关节周围神经末梢退变相关,而应用柴胡桂枝汤合玻璃酸钠注射液治疗可以获得较为满意的痛点镇痛效果。展开更多
文摘A considerable portion of the population now experiences osteoarthritis of the knee,spine,and hip due to lifestyle changes.Therefore,early treatment,recognition and prevention are essential to reduce damage;nevertheless,this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians.To overcome the existing challenges in the early detection of Knee Osteoarthritis(KOA),an effective automated technique,prompt recognition,and correct categorization are required.This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence(KL) classification schemes,such as Class-I,Class-II,Class-III,and Class-IV.Utilizing ResNet to segregate knee pictures,we first collected features from these images before using the Bidirectional Long Short-Term Memory(BiLSTM)architecture to classify them.Given that the technique is a pre-trained network and doesn’t require a large training set,the Mendeley VI dataset has been utilized for the training of the proposed model.To evaluate the effectiveness of the suggested model,cross-validation has also been employed using the Osteoarthritis Initiative(OAI)dataset.Furthermore,our suggested technique is more resilient,which overcomes the challenge of imbalanced training data due to the hybrid architecture of our proposed model.The suggested algorithm is a cuttingedge and successful method for documenting the successful application of the timely identification and severity categorization of KOA.The algorithm showed a cross-validation accuracy of 78.57%and a testing accuracy of 84.09%.Numerous tests have been conducted to show that our suggested algorithm is more reliable and capable than the state-of-the-art at identifying and categorizing KOA disease.
文摘膝骨性关节炎（KOA ）又称膝骨关节病、骨关节炎、退行性关节炎、或增生性骨关节炎骨关节病。KOA的主要病理改变为软骨退行性变性和消失，以及关节边缘韧带附着处和软骨下骨质反应性增生形成骨赘，病变常累及整个关节和周围组织并由此引起关节疼痛、僵直、畸形和关节障碍。该病是常见的一种关节病变，发病率约占整个骨关节炎（O A ）发病率的80％，K O A发病与很多因素有关，如年龄、体重、骨密度、创伤、内分泌等。其发病率随着年龄而增加，女性比男性多发。国外调查指出，有明显骨关节炎X线片证据者，在45～64岁年龄组中，男性占25％，女性占30％；而在65岁以上年龄组中，男性上升为58％，女性上升为65％。通过临床调查也证实，骨关节炎的发病率在59～69岁之间占29％，而在75岁或以上的占70％。随着人口老龄化程度的加深，这一问题将会愈加严重。1999年世界卫生组织把骨性关节炎与心血管疾病及癌症列为危害人类健康的三大杀手，将2000-2010年定为“骨与关节十年”，并将每年的10月12日定为“国际关节炎日”［1］。探索有效的治疗方法，是临床医疗工作者一直追求的目标，也是本课题的最终目的。
文摘目的：探究中期KOA患者痛点规律及在玻璃酸钠膝关节注射基础上对比柴胡桂枝汤与地塞米松磷酸钠合利多卡因对中期KOA患者痛点镇痛效果。方法：2012年3月—2015年3月就诊于云南省中医医院和昆明市中医医院骨科符合KOA诊断标准及纳入标准患者154例,随机分为对照组（77例、91膝）和研究组（77例、93膝）。2组患者均予玻璃酸钠注射液膝关节腔注射1次/周,连用4周;对照组加用地塞米松磷酸钠注射液合利多卡因注射液于第1周及第4周（必要时）;研究组口服柴胡桂枝汤100 m L,tid,连用1周间隔1周后再用1周,共用2周。4周后对比2组治疗前后痛点VAS疼痛评分（9分法）及临床疗效。结果：研究组有效率（67.1%）高于对照组（58.3%）,且研究组（27.1%）治疗期间止痛药应用情况低于对照组（41.6%）;按照膝关节9分法分区,中期KOA痛点多集中于外侧,治疗前各区痛点VAS评分无差异（P〉0.05）,治疗后研究组外侧区及全膝痛点VAS评分低于对照组（P〈0.05）。结论：KOA中期的基本病机可能在于＂枢机不利、气血痹阻＂,同时可能与膝关节周围神经末梢退变相关,而应用柴胡桂枝汤合玻璃酸钠注射液治疗可以获得较为满意的痛点镇痛效果。
