Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee ...Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.展开更多
BACKGROUND Discoid meniscus is a congenital anomaly that typically affects the lateral meniscus.The appearance of the discoid medial meniscus in both knees is extremely rare,with an incidence of only 0.012%.CASE SUMMA...BACKGROUND Discoid meniscus is a congenital anomaly that typically affects the lateral meniscus.The appearance of the discoid medial meniscus in both knees is extremely rare,with an incidence of only 0.012%.CASE SUMMARY Our patient was a 30-year-old female.Under no obvious predisposing causes,she began to experience pain in both knees,which worsened while walking and squatting.The pain was aggravated after exercise,and joint flexion and extension activities were accompanied by knee snapping.Apley’s test was positive on physical examination,and there was a pressing pain in the medial articular space.Plain radiographs of both knees revealed no obvious abnormalities in the bilateral knee joint space.Partial meniscectomy as well as menisci reformation were performed on both knees under arthroscopy.Under the guidance of rehabilitation,the patient’s range of motion in both knees returned to normal without pain and knee snapping.CONCLUSION This study showed that the clinical manifestations of the discoid medial meniscus injury are identical to those of the conventional medial meniscus injury,and arthroscopic surgery is effective.展开更多
X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is ...X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.展开更多
目的基于磁共振mDIXON-Quant技术分析膝骨关节炎(knee osteoarthritis,KOA)髌下脂肪垫质子密度脂肪分数(proton density fat fraction,PDFF)改变,及其与KOA严重程度的相关性。材料与方法前瞻性招募44例KOA患者,共计对70例膝关节进行磁...目的基于磁共振mDIXON-Quant技术分析膝骨关节炎(knee osteoarthritis,KOA)髌下脂肪垫质子密度脂肪分数(proton density fat fraction,PDFF)改变,及其与KOA严重程度的相关性。材料与方法前瞻性招募44例KOA患者,共计对70例膝关节进行磁共振常规序列及mDIXON-Quant序列扫描,测量KOA患者髌下脂肪垫PDFF。膝关节采用全器官磁共振评分(whole-organ magnetic resonance imaging score,WORMS)评估受试KOA严重程度。分析各膝髌下脂肪垫PDFF与WORMS 11个特征独立评分及总评分的相关性。结果髌下脂肪垫PDFF与膝关节WORMS总评分、关节软骨完整性、边缘骨赘、关节面下骨磨损、关节面下骨髓异常、关节面下骨囊肿、内外侧半月板完整性、关节内游离体、关节周围囊肿/滑囊炎、前后交叉韧带完整性、滑膜炎/积液(r=-0.94、-0.85、-0.83、-0.80、-0.72、-0.52、-0.54、-0.39、-0.27、-0.27、-0.24,P均<0.05)WORMS评分均呈负相关性;与内外侧副韧带完整性(r=0.27,P=0.826)WORMS评分无显著相关性。观察者间一致性非常好ICC=0.793(P<0.001),95%可信区间为(0.667~0.875)。结论磁共振mDIXON-Quant技术可以定量评估KOA患者髌下脂肪垫PDFF改变,髌下脂肪垫PDFF随着KOA严重程度进展而下降。髌下脂肪垫PDFF可以作为反映KOA严重程度的客观评价指标。展开更多
基金The authors extend their appreciation to the Jilin Provincial Natural Science Foundation for funding this research work through Project Number(20220101128JC).
文摘Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.
基金Supported by the National Natural Science Foundation of China,No.81871814Jining City Key Research and Development Plan,No.2021YXNS076。
文摘BACKGROUND Discoid meniscus is a congenital anomaly that typically affects the lateral meniscus.The appearance of the discoid medial meniscus in both knees is extremely rare,with an incidence of only 0.012%.CASE SUMMARY Our patient was a 30-year-old female.Under no obvious predisposing causes,she began to experience pain in both knees,which worsened while walking and squatting.The pain was aggravated after exercise,and joint flexion and extension activities were accompanied by knee snapping.Apley’s test was positive on physical examination,and there was a pressing pain in the medial articular space.Plain radiographs of both knees revealed no obvious abnormalities in the bilateral knee joint space.Partial meniscectomy as well as menisci reformation were performed on both knees under arthroscopy.Under the guidance of rehabilitation,the patient’s range of motion in both knees returned to normal without pain and knee snapping.CONCLUSION This study showed that the clinical manifestations of the discoid medial meniscus injury are identical to those of the conventional medial meniscus injury,and arthroscopic surgery is effective.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning (KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.
文摘目的基于磁共振mDIXON-Quant技术分析膝骨关节炎(knee osteoarthritis,KOA)髌下脂肪垫质子密度脂肪分数(proton density fat fraction,PDFF)改变,及其与KOA严重程度的相关性。材料与方法前瞻性招募44例KOA患者,共计对70例膝关节进行磁共振常规序列及mDIXON-Quant序列扫描,测量KOA患者髌下脂肪垫PDFF。膝关节采用全器官磁共振评分(whole-organ magnetic resonance imaging score,WORMS)评估受试KOA严重程度。分析各膝髌下脂肪垫PDFF与WORMS 11个特征独立评分及总评分的相关性。结果髌下脂肪垫PDFF与膝关节WORMS总评分、关节软骨完整性、边缘骨赘、关节面下骨磨损、关节面下骨髓异常、关节面下骨囊肿、内外侧半月板完整性、关节内游离体、关节周围囊肿/滑囊炎、前后交叉韧带完整性、滑膜炎/积液(r=-0.94、-0.85、-0.83、-0.80、-0.72、-0.52、-0.54、-0.39、-0.27、-0.27、-0.24,P均<0.05)WORMS评分均呈负相关性;与内外侧副韧带完整性(r=0.27,P=0.826)WORMS评分无显著相关性。观察者间一致性非常好ICC=0.793(P<0.001),95%可信区间为(0.667~0.875)。结论磁共振mDIXON-Quant技术可以定量评估KOA患者髌下脂肪垫PDFF改变,髌下脂肪垫PDFF随着KOA严重程度进展而下降。髌下脂肪垫PDFF可以作为反映KOA严重程度的客观评价指标。
