Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewpriv...Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.展开更多
Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The signif...Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.展开更多
[Objective] This study aimed to establish a new method for preparing paraffin sections of cattle eyebal s. [Method] The conventional method was used to prepare paraffin sections for cattle eyebal s in the control and ...[Objective] This study aimed to establish a new method for preparing paraffin sections of cattle eyebal s. [Method] The conventional method was used to prepare paraffin sections for cattle eyebal s in the control and a new method termed＆quot;opening a window on cornea and refixation＆quot; was used to prepare paraffin sections for cattle eyebal s in the treatment group. [Result] After the prepared specimens in the treatment group were fixed, it could be macroscopical y observed that retina and choroid were closely connected, with detachment occurring at a smal portion be-tween the two. According to the paraffin sections, it was microscopical y observed that the continuity of trabecular meshwork was intact, as wel as the continuity be-tween different layers of eyebal wal , without detachment between them, no retinal detachment, no shrinkage of each layer of tissue cells. [Conclusion] This study pro-vides a foundation for the basic research and pathological study of eyebal s.展开更多
Purpose: To explore the pathological changes of retinal ganglion cell apoptosis and its relation to the value of glutamate concentration in vitreous body after explosive injury of eyeballs in the rabbits.Method: Simil...Purpose: To explore the pathological changes of retinal ganglion cell apoptosis and its relation to the value of glutamate concentration in vitreous body after explosive injury of eyeballs in the rabbits.Method: Similar explosive injury models of eyeballs in 10 adult grey rabbits were made.The rabbits were killed on scheduled time. The retinal tissues of studied eyes and control eyes were obtained for the pathological examination with TUNEL method respectively.The value of glutamate in vitreum of injured eyes was measured and was compared with that of contralateral eyes. Statistical comparison analysis on the experiment data was performed.Result: The value of glutamate in vitreum of injured eyes was significantly higher than that of contralateral eyes in all rabbits in the study. A lot of TUNEL positive cells were observed in the injured eyes. It suggested that apoptosis of the retinal ganglion cells took place.Conclusion: We speculate that apoptosis of the retinal ganglion cells is perhaps among the causes resulting in damage of visual function after explosive injury of eyeballs and that the increasing of the value of glutamate in vitreum possibly associated with apoptosis of the retinal ganglion cells.展开更多
Objective:To establish rabbit eyeball rupture model by air gun in order to observe and analyze the early injury condition and reasons of retinal cell after eyeball rupture.Methods:Forty eight healthy rabbits were rand...Objective:To establish rabbit eyeball rupture model by air gun in order to observe and analyze the early injury condition and reasons of retinal cell after eyeball rupture.Methods:Forty eight healthy rabbits were randomly divided into control group and 1,3,6,12 and 24 h after injury groups.After anesthesia,the rabbit eyeball rupture model was established by air gun. Then the early pathological changes of rabbit retina were observed,and apoptotic index(Al), oncosis index(OI),the relationship between the expression amounts of apoptosis-related genes and Al were analyzed.Results:Obvious pathological lesion appeared in retina 6 h after injury. Irreversible damage occurred 12-24 h after injury.The results of AI and OI indicated that the OI peak appeared 6 h after injury and then gradually declined,while the AI increased with the prolongation of time,and the AI was higher than OI in 12 h after injury.Immunohistochemical results indicated that there was no obvious bcl-2 protein expression change.Compared with the control group and the 3,6,12 and 24 h after the injury groups,the expressions of p53 and Caspase-3 were significandy improved and peaked at 12 h(P【0.01).Positive correlation existed among p53,Caspase-3 expression amount and cell apoptosis amount.Conclusions:Apoptosis and oncosis of visual cells are the main reasons of retinal cell injury.p53 and Caspase-3 are the important factors in promoting the retinal cell apoptosis after eyeball rupture.展开更多
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-ti...Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.展开更多
Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the fi...Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.展开更多
In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relationa...In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.展开更多
Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency o...Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.展开更多
AIM:To compare the exposure rate,infection rate,percentage of enhancement,and success rate between Medpor and the three-dimensional printed polyethylene(3DP-PE)orbital implant in a preliminary report.METHODS:This pros...AIM:To compare the exposure rate,infection rate,percentage of enhancement,and success rate between Medpor and the three-dimensional printed polyethylene(3DP-PE)orbital implant in a preliminary report.METHODS:This prospective,randomized,equivalence,controlled trial was conducted at two institutes.The equivalent margin was±10%.The sample size for the equivalence trial was 174 participants per group.Patients who were eligible for enucleations received either Medpor or 3DP-PE implants based on a randomized block of six.The surgeries were performed by five oculoplastic surgeons.The assessor and patients were masked.The magnetic resonance imaging(MRI)of the orbit was performed at least 6mo after operation and the fibrovascular ingrowth was analyzed using the Image J software.Follow-up continued at least 1y after surgery.The intention to treat and per protocol approaches were used.RESULTS:Totally 128 patients met the criteria in the report.Fifty Medpor and 553DP-PE cases completed the trial.The most common cause of blindness was trauma.The mean follow-up times of Medpor and 3DP-PE were 33 and 40mo respectively.The exposure rate was not statistically significant between two groups(6.0%and 7.3%),P<0.05,95%CI(-9.8%,+12.0%).The success rates were 94%(Medpor)and 92.7%(3DP-PE).No postoperative infection was reported.Nine patients had MRI tests and two had implant exposures with 66.3% enhancement at 75mo(Medpor)and 58% enhancement at 57mo(3DP-PE)postoperatively.CONCLUSION:There is no statistically significant difference in exposure rate and success rate between Medpor and 3DP-PE in enucleation in the report.However,we cannot conclude that they are equivalent in terms of the exposure rate and success rate because the 95%CI is wider than±10%.The infection rate is equivalent in both groups.展开更多
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
文摘Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.
文摘Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.
基金Supported by China Agriculture Research System(CARS-38)~~
文摘[Objective] This study aimed to establish a new method for preparing paraffin sections of cattle eyebal s. [Method] The conventional method was used to prepare paraffin sections for cattle eyebal s in the control and a new method termed＆quot;opening a window on cornea and refixation＆quot; was used to prepare paraffin sections for cattle eyebal s in the treatment group. [Result] After the prepared specimens in the treatment group were fixed, it could be macroscopical y observed that retina and choroid were closely connected, with detachment occurring at a smal portion be-tween the two. According to the paraffin sections, it was microscopical y observed that the continuity of trabecular meshwork was intact, as wel as the continuity be-tween different layers of eyebal wal , without detachment between them, no retinal detachment, no shrinkage of each layer of tissue cells. [Conclusion] This study pro-vides a foundation for the basic research and pathological study of eyebal s.
文摘Purpose: To explore the pathological changes of retinal ganglion cell apoptosis and its relation to the value of glutamate concentration in vitreous body after explosive injury of eyeballs in the rabbits.Method: Similar explosive injury models of eyeballs in 10 adult grey rabbits were made.The rabbits were killed on scheduled time. The retinal tissues of studied eyes and control eyes were obtained for the pathological examination with TUNEL method respectively.The value of glutamate in vitreum of injured eyes was measured and was compared with that of contralateral eyes. Statistical comparison analysis on the experiment data was performed.Result: The value of glutamate in vitreum of injured eyes was significantly higher than that of contralateral eyes in all rabbits in the study. A lot of TUNEL positive cells were observed in the injured eyes. It suggested that apoptosis of the retinal ganglion cells took place.Conclusion: We speculate that apoptosis of the retinal ganglion cells is perhaps among the causes resulting in damage of visual function after explosive injury of eyeballs and that the increasing of the value of glutamate in vitreum possibly associated with apoptosis of the retinal ganglion cells.
基金supported by the National Natural Science Eoundation of China(81071723)
文摘Objective:To establish rabbit eyeball rupture model by air gun in order to observe and analyze the early injury condition and reasons of retinal cell after eyeball rupture.Methods:Forty eight healthy rabbits were randomly divided into control group and 1,3,6,12 and 24 h after injury groups.After anesthesia,the rabbit eyeball rupture model was established by air gun. Then the early pathological changes of rabbit retina were observed,and apoptotic index(Al), oncosis index(OI),the relationship between the expression amounts of apoptosis-related genes and Al were analyzed.Results:Obvious pathological lesion appeared in retina 6 h after injury. Irreversible damage occurred 12-24 h after injury.The results of AI and OI indicated that the OI peak appeared 6 h after injury and then gradually declined,while the AI increased with the prolongation of time,and the AI was higher than OI in 12 h after injury.Immunohistochemical results indicated that there was no obvious bcl-2 protein expression change.Compared with the control group and the 3,6,12 and 24 h after the injury groups,the expressions of p53 and Caspase-3 were significandy improved and peaked at 12 h(P【0.01).Positive correlation existed among p53,Caspase-3 expression amount and cell apoptosis amount.Conclusions:Apoptosis and oncosis of visual cells are the main reasons of retinal cell injury.p53 and Caspase-3 are the important factors in promoting the retinal cell apoptosis after eyeball rupture.
基金supported in part by the National Natural Science Foundation of China(Grant No.82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)of Shenzhen Science and Technology Innovation Committee+6 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Natural Science Foundation of Jiangsu Province(No.BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038 and SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575)the Henan Province Science and Technology Research(222102310322)The Jiangsu Students’Innovation and Entrepreneurship Training Program(202110304096Y).
文摘Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.
基金supported in part by the Key Program of NSFC (Grant No.U1908214)Special Project of Central Government Guiding Local Science and Technology Development (Grant No.2021JH6/10500140)+3 种基金Program for the Liaoning Distinguished Professor,Program for Innovative Research Team in University of Liaoning Province (LT2020015)Dalian (2021RT06)and Dalian University (XLJ202010)the Science and Technology Innovation Fund of Dalian (Grant No.2020JJ25CY001)Dalian University Scientific Research Platform Project (No.202101YB03).
文摘Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios.With the introduction of end-to-end direct regression methods,the field has entered a new stage of development.However,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal method.In this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external factors.Specifically,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints.We call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.
文摘In multi-view image localization task,the features of the images captured from different views should be fused properly.This paper considers the classification-based image localization problem.We propose the relational graph location network(RGLN)to perform this task.In this network,we propose a heterogeneous graph construction approach for graph classification tasks,which aims to describe the location in a more appropriate way,thereby improving the expression ability of the location representation module.Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin.In addition,the proposed localization method outperforms the compared localization methods by around 1.7%in terms of meter-level accuracy.
基金This work was supported by the National Natural Science Foundation of China(62073087,62071132,61973090).
文摘Deep matrix factorization(DMF)has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data(MDR).However,existing multiview DMF methods mainly explore the consistency of multi-view data,while neglecting the diversity among different views as well as the high-order relationships of data,resulting in the loss of valuable complementary information.In this paper,we design a hypergraph regularized diverse deep matrix factorization(HDDMF)model for multi-view data representation,to jointly utilize multi-view diversity and a high-order manifold in a multilayer factorization framework.A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data.Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view.An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis.Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms stateof-the-art multi-view learning approaches.
基金Supported by the Mettapracharak grantThai Government Budget grant+1 种基金Health Systems Research Institute grantNational Science and Technology Development Agency grant.
文摘AIM:To compare the exposure rate,infection rate,percentage of enhancement,and success rate between Medpor and the three-dimensional printed polyethylene(3DP-PE)orbital implant in a preliminary report.METHODS:This prospective,randomized,equivalence,controlled trial was conducted at two institutes.The equivalent margin was±10%.The sample size for the equivalence trial was 174 participants per group.Patients who were eligible for enucleations received either Medpor or 3DP-PE implants based on a randomized block of six.The surgeries were performed by five oculoplastic surgeons.The assessor and patients were masked.The magnetic resonance imaging(MRI)of the orbit was performed at least 6mo after operation and the fibrovascular ingrowth was analyzed using the Image J software.Follow-up continued at least 1y after surgery.The intention to treat and per protocol approaches were used.RESULTS:Totally 128 patients met the criteria in the report.Fifty Medpor and 553DP-PE cases completed the trial.The most common cause of blindness was trauma.The mean follow-up times of Medpor and 3DP-PE were 33 and 40mo respectively.The exposure rate was not statistically significant between two groups(6.0%and 7.3%),P<0.05,95%CI(-9.8%,+12.0%).The success rates were 94%(Medpor)and 92.7%(3DP-PE).No postoperative infection was reported.Nine patients had MRI tests and two had implant exposures with 66.3% enhancement at 75mo(Medpor)and 58% enhancement at 57mo(3DP-PE)postoperatively.CONCLUSION:There is no statistically significant difference in exposure rate and success rate between Medpor and 3DP-PE in enucleation in the report.However,we cannot conclude that they are equivalent in terms of the exposure rate and success rate because the 95%CI is wider than±10%.The infection rate is equivalent in both groups.
