Classification of MDD using a Transformer classifier with large-scale multisite resting-state fMRI data.

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide with high recurrence rate. Identifying MDD patients, particularly those with recurrent episodes with resting-state fMRI, may reveal the relationship between MDD and brain function. We proposed a Transformer-Encoder model, which utilized functional connectivity extracted from large-scale multisite rs-fMRI datasets to classify MDD and HC. The model discarded the Transformer's Decoder part, reducing the model's complexity and decreasing the number of parameters to adapt to the limited sample size and it does not require a complex feature selection process and achieves end-to-end classification. Additionally, our model is suitable for classifying data combined from multiple brain atlases and has an optional unsupervised pre-training module to acquire optimal initial parameters and speed up the training process. The model's performance was tested on a large-scale multisite dataset and identified brain regions affected by MDD using the Grad-CAM method. After conducting five-fold cross-validation, our model achieved an average classification accuracy of 68.61% on a dataset consisting of 1611 samples. For the selected recurrent MDD dataset, the model reached an average classification accuracy of 78.11%. Abnormalities were detected in the frontal gyri and cerebral cortex of MDD patients in both datasets. Furthermore, the identified brain regions in the recurrent MDD dataset generally exhibited a higher contribution to the model's performance.

Integrating model explanations and hybrid priors into deep stacked networks for the "safe zone" prediction of acetabular cup.

BACKGROUND: Existing state-of-the-art "safe zone" prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability. PURPOSE: To explore the model explanations and estimator consensus for "safe zone" prediction. MATERIAL AND METHODS: We collected the pelvic datasets from Orthopaedic Hospital, and a novel acetabular cup detection method is proposed for automatic ROI segmentation. Hybrid priors comprising both specific priors from data and general priors from experts are constructed. Specifically, specific priors are constructed based on the fine-tuned ResNet-101 convolutional neural networks (CNN) model, and general priors are constructed based on expert knowledge. Our method considers the model explanations and dynamic consensus through appending a SHapley Additive exPlanations (SHAP) module and a dynamic estimator stacking. RESULTS: The proposed method achieves an accuracy of 99.40% and an area under the curve of 0.9998. Experimental results show that our model achieves superior results to the state-of-the-art conventional ensemble classifiers and deep CNN models. CONCLUSION: This new screening model provides a new option for the "safe zone" prediction of acetabular cup.

Angiogenesis and flap-related research: A bibliometric analysis.

Adequate blood supply, a prerequisite for flap survival after grafting, makes angiogenesis of the flap the biggest problem to be solved. Researches have been conducted around vascularisation in correlation with flap grafting. However, bibliometric analyses systematically examining this research field are lacking. As such, we herein sought to conduct comprehensive comparative analyses of the contributions of different researchers, institutions, and countries to this research space in an effort to identify trends and hotspots in angiogenesis and vascularisation in the context of flap grafting. Publications pertaining to angiogenesis and vascularisation in the context of flap grafting were retrieved from the Web of Science Core Collection. References were then analysed and plotted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. In total, 2234 papers that were cited 40 048 times (17.63 citations/paper) were included in this analysis. The greatest number of studies were from the United States, with these studies exhibiting both the highest number of citations (13 577) and the greatest overall H-index (60). For The institutions that published the greatest number of studies were WENZHOU MEDICAL UNIVERSITY (681), while UNIVERSITY OF ERLANGEN NUREMBERG has the highest number of citations (1458), and SHANGHAI JIAO TONG UNIVERSITY holds the greatest overall H-index (20). The greatest number of studies in this research space were published by Gao WY, while Horch RE was the most commonly cited researcher in the field. The VOS viewer software clustered relevant keywords into three clusters, with clusters 1, 2, 3, and 4 corresponding to studies in which the keywords 'anatomy', 'survival', 'transplantation', 'therapy' most frequently appeared. The most promising research hotspot-related terms in this field included 'autophagy', 'oxidative stress', 'ischemia/reperfusion injury', which exhibited a most recent average appearing year (AAY) of 2017 and after. Generally speaking, the results of this analysis indicate that the number of articles exploring angiogenesis and flap-related research has risen steadily, with the United States and China being the two countries publishing the greatest proportion of studies in this field. The overall focus of these studies has shifted away from 'infratest and tissue engineering' towards 'mechanisms'. In the future, particular attention should be paid to emerging research hotspots, which include 'ischemia/reperfusion injury' and treatments for promoting vascularization, such as 'platelet-rich plasma'. In light of these findings, funding agencies should continue increasing their investment in the exploration of the concrete mechanisms and interventional therapeutic relevance of angiogenesis during flap transplantation.

Noninvasive Machine Learning Screening Model for Dacryocystitis Based on Ocular Surface Indicators.

BACKGROUND: Dacryocystitis is an orbital disease that can be easily misdiagnosed. The most common diagnostic tools for dacryocystitis are computed tomography, lacrimal duct angiography, and lacrimal tract irrigation. Yet, those are invasive methods, which are not conducive to extensive screening. OBJECTIVE: To explore the significance of ocular surface indicators and demographic data in the screening of dacryocystitis. MATERIALS AND METHODS: Data were prospectively collected from 56 patients with dacryocystitis (56 eyes) and 56 healthy individuals. Collected indicators included demographic information (gender, age), ocular surface data of tear meniscus height, objective scatter index (OSI), and clinical diagnosis. The model features were screened out by machine learning to establish a dacryocystitis screening model. RESULTS: Tear meniscus height, OSI_maximum Lyapunov exponent, basic OSI, median of OSI, mean of OSI, slope coefficient of OSI linear regression, coefficient of variation in OSI, interquartile range of OSI, and other 8 parameters were used as model parameters to establish a dacryocystitis screening model with an overall detection accuracy of 85.71%. CONCLUSIONS: This new screening model that is based on ocular surface indicators provides a new option for noninvasive screening of dacryocystitis.

Explainable Prediction of Dacryocystitis From Noninvasive Ocular Indicators Using Deep Stacked Network and The Shapley Additive Explanations Approach.

ABSTRACT: Dacryocystitis diagnosis is important for preventing rapid blurring and vision loss. Existing state-of-the-art methods focus on routine clinical examinations and objective scattering index-based statistical analysis. Such approaches are invasive operations or lack quantitative indicators, and their application is limited. in addition, little attention has been paid to the explainability and clinical utility of models. This paper proposes an explainable dacryocystitis prediction model from noninvasive ocular indicators. The proposed model is based on an deep stacked network with 4 improvements: a multivariable feature extraction module, obtaining comprehensive predictive factors including the quantitative ocular indictors, conventional texture features, and deep learning features from shallow to deep convolutional layers; a multifeature fusion and attribute selection module based on the ReliefF method, guiding the network to focus on useful information at variables; Decision curve analysis the model is introduced into the model to evaluates the risks and benefits; and appending a SHapley Additive exPlanations (SHAP) module to the framework to automatically and efficiently interpret the prediction of the models. By integrating the above improvements in series, the models' performances are gradually enhanced. Real labeled data samples are used to train and test the model, and our model achieves high accuracy and reliability.

Automatic Real-Time Space Registration Application for Simulating Dental and Maxillofacial Surgery.

ABSTRACT: Real-time surgical navigation systems are important for preoperative planning and intraoperative navigation. Automatic preoperative multimodal data registration and postoperative spatial registration are extremely crucial in such surgical navigation systems. However, existing automatic multimodal data registration methods have extremely limited application scope due to the lack of accuracy and speed. In addition, the registration results obtained by existing methods are practically lacking and are rarely applied in clinics. To address the above issues, this paper proposes a novel real-time teeth registration algorithm with computed tomography (CT) data and optical tracking scanning data. The proposed method is based on the weighted iterative closest point (ICP) algorithm with 3 improvements: (1) the multilayer spherical point set is generated inside the laser scanning marker sphere, (2) the weight decreases from inside to outside layer by layer, and (3) the weight of the voxel center point set is combined with the CT data of the marker sphere. Specifically, the proposed iCP registration method can overcome the limitation of surface point set registration and tackle the problem of high surface deformity of laser scanning marker spheres. For the registration result of CT and scanning data, the authors employ the real-time spatial registration algorithm based on optical tracking to complete the navigation of the simulated surgical instruments on the multimodal fusion image. The experimental results show that the proposed ICP algorithm reduces the mean square error by 1 order of magnitude and that our method has strong practical value.

Synchrotron radiation micro-tomography for high-resolution neurovascular network morphology investigation.

There has been increasing interest in using high-resolution micro-tomography to investigate the morphology of neurovascular networks in the central nervous system, which remain difficult to characterize due to their microscopic size as well as their delicate and complex 3D structure. Synchrotron radiation X-ray imaging, which has emerged as a cutting-edge imaging technology with a high spatial resolution, provides a novel platform for the non-destructive imaging of microvasculature networks at a sub-micrometre scale. When coupled with computed tomography, this technique allows the characterization of the 3D morphology of vasculature. The current review focuses on recent progress in developing synchrotron radiation methodology and its application in probing neurovascular networks, especially the pathological changes associated with vascular abnormalities in various model systems. Furthermore, this tool represents a powerful imaging modality that improves our understanding of the complex biological interactions between vascular function and neuronal activity in both physiological and pathological states.

3D Computed Tomography Mapping of Thoracolumbar Vertebrae Fractures.

BACKGROUND Fractures of the thoracolumbar (TL) spine represent 90% of all spinal fractures, followed by cervical and lumbar spine fractures. This study aimed to create fracture maps of the traumatic thoracolumbar (TL) fracture vertebral body (T12-L2) through the use of CT mapping as a big data visualization method to reveal recurrent patterns and characteristics of traumatic TL fractures. MATERIAL AND METHODS A consecutive series of 174 fractured vertebrae (T12-L2) was used to create three-dimensional (3D) reconstruction images, which were superimposed and oriented to fit a model vertebral template by aligning specific bio-landmarks and reducing reconstructed fracture fragments. Fracture lines were found and traced to create a fracture map of the vertebral body. RESULTS Our study consisted of 165 patients with an average age of 47 years. A total of 174 fractured vertebrae were collected, consisting of 59 T12 vertebral fractures, 60 L1 vertebral fractures, and 55 L2 vertebral fractures. Two-dimensional (2D) maps, 3D maps, and heat maps showed that the fracture lines tended to be concentrated in the upper third and anterior third of the vertebral body, as well as being distributed in annular wedges along the anterior and lateral sides of the vertebral body. When compared with T12, the distribution of fracture lines in L1 and especially in L2 was more scattered and disorganized. CONCLUSIONS Fracture maps revealed recurrent patterns and characteristics of the traumatic TL fracture vertebral body, which improves understanding of TL fractures, as well as helping to increase opportunities for follow-up research and aid clinical decision-making.

The 3D characteristics of post-traumatic syringomyelia in a rat model: a propagation-based synchrotron radiation microtomography study.

Many published literature sources have described the histopathological characteristics of post-traumatic syringomyelia (PTS). However, three-dimensional (3D) visualization studies of PTS have been limited due to the lack of reliable 3D imaging techniques. In this study, the imaging efficiency of propagation-based synchrotron radiation microtomography (PB-SRµCT) was determined to detect the 3D morphology of the cavity and surrounding microvasculature network in a rat model of PTS. The rat model of PTS was established using the infinite horizon impactor to produce spinal cord injury (SCI), followed by a subarachnoid injection of kaolin to produce arachnoiditis. PB-SRµCT imaging and histological examination, as well as fluorescence staining, were conducted on the animals at the tenth week after SCI. The 3D morphology of the cystic cavity was vividly visualized using PB-SRµCT imaging. The quantitative parameters analyzed by PB-SRµCT, including the lesion and spared spinal cord tissue area, the minimum and maximum diameters in the cystic cavity, and cavity volume, were largely consistent with the results of the histological assessment. Moreover, the 3D morphology of the cavity and surrounding angioarchitecture could be simultaneously detected on the PB-SRµCT images. This study demonstrated that high-resolution PB-SRµCT could be used for the 3D visualization of trauma-induced spinal cord cavities and provides valuable quantitative data for cavity characterization. PB-SRµCT could be used as a reliable imaging technique and offers a novel platform for tracking cavity formation and morphological changes in an experimental animal model of PTS.

Nanostring-cluster hierarchical structured Bi2O3: synthesis, evolution and application in biosensing.

In the present study, a simple strategy was developed to fabricate a new Bi2O3 nanostring-cluster hierarchical structure. Precursor microrods composed of Bi(C2O4)OH were initially grown under hydrothermal conditions. After calcination in air, Bi(C2O4)OH microrods were carved into unique string-cluster structures by the gas produced during the decomposition process. To explain the formation mechanism, the effects of pyrolysis temperature and time on the morphology of the as-prepared samples were investigated and are discussed in detail. It was discovered that the nanostring-cluster-structured Bi2O3 consists of thin nanoplatelet arrays, which is advantageous for glucose enzyme immobilization and for designing biosensors. The resulting Bi2O3 structure showed an excellent capability in the modification of electrode surfaces in biosensors by enhancing the sensitivity, with good specificity and response time. Such qualities of a biosensor are ideal characteristics for glucose sensing performance and allow for further explorations of its application in other fields.

Biomechanical analysis of press-extension technique on degenerative lumbar with disc herniation and staggered facet joint.

This study investigates the effect of a new Chinese massage technique named "press-extension" on degenerative lumbar with disc herniation and facet joint dislocation, and provides a biomechanical explanation of this massage technique. Self-developed biomechanical software was used to establish a normal L1-S1 lumbar 3D FE model, which integrated the spine CT and MRI data-based anatomical structure. Then graphic technique is utilized to build a degenerative lumbar FE model with disc herniation and facet joint dislocation. According to the actual press-extension experiments, mechanic parameters are collected to set boundary condition for FE analysis. The result demonstrated that press-extension techniques bring the annuli fibrosi obvious induction effect, making the central nucleus pulposus forward close, increasing the pressure in front part. Study concludes that finite element modelling for lumbar spine is suitable for the analysis of press-extension technique impact on lumbar intervertebral disc biomechanics, to provide the basis for the disease mechanism of intervertebral disc herniation using press-extension technique.

Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.

This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentation of stroke lesions in medical images such as MRI and CT scans is crucial for diagnosis, treatment planning and prognosis. This paper first introduces common imaging modalities used for stroke diagnosis, discussing their capabilities in imaging lesions at different disease stages from the acute to chronic stage. It then reviews three major public benchmark datasets for evaluating stroke segmentation algorithms: ATLAS, ISLES and AISD, highlighting their key characteristics. The paper proceeds to provide an overview of foundational deep learning architectures for medical image segmentation, including CNN-based and transformer-based models. It summarizes recent innovations in adapting these architectures to the task of stroke lesion segmentation across the three datasets, analyzing their motivations, modifications and results. A survey of loss functions and data augmentations employed for this task is also included. The paper discusses various aspects related to stroke segmentation tasks, including prior knowledge, small lesions, and multimodal fusion, and then concludes by outlining promising future research directions. Overall, this comprehensive review covers critical technical developments in the field to support continued progress in automated stroke lesion segmentation.

Elastic parameter identification of three-dimensional soft tissue based on deep neural network.

In the field of virtual surgery and deformation simulation, the identification of elastic parameters of human soft tissues is a critical technology that directly affects the accuracy of deformation simulation. Current research on soft tissue deformation simulation predominantly assumes that the elasticity of tissues is fixed and already known, leading to the difficulty in populating with the elasticity measured or identified from specific tissues of real patients. Existing elasticity modeling efforts struggle to be implemented on irregularly structured soft tissues, failing to adapt to clinical surgical practices. Therefore, this paper proposes a new method for identifying human soft tissue elastic parameters based on the finite element method and the deep neural network, UNet. This method requires only the full-field displacement data of soft tissues under external loads to predict their elastic distribution. The performance and validity of the algorithm are assessed using test data and clinical data from rhinoplasty surgeries. Experiments demonstrate that the method proposed in this paper can achieve an accuracy of over 99% in predicting elastic parameters. Clinical data validation shows that the predicted elastic distribution can reduce the error in finite element deformation simulations by more than 80% at the maximum compared to the error with traditional uniform elastic parameters, effectively enhancing the computational accuracy in virtual surgery simulations and soft tissue deformation modeling.

Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data.

BACKGROUND AND OBJECTIVE: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. METHODS: We developed a Residual Denoising Autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over-sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. RESULTS: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. CONCLUSIONS: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

[FEM simulation of complex lumbar spinal stenosis decompression surgery].

This paper aims to establish an accurate finite element model of complete lumbar spine with complex lumbar spinal stenosis (LSS), and then to do comparison and analysis of normal model and decompression surgery model. Firstly, we chose some patients with complex LSS and then collected the CT scanned data. Then we generated a complete FE model of Lumbar with complex LSS using a specially designed modeling system, and we also created a normal lumbar model and a decompression treated model. We applied same boundary conditions in all the three models. The results showed that the active movement range of complex LSS was smaller than that of the normal model, but the movement range of the decompression model was larger than that of the normal. There are stress concentration around the endplate and disk at the degenerative intervertebral? disk L4-L5 and adjacent disk L3-L4 for LSS model, and the stress of the decompression model increased more significantly. This simulation demonstrated that the treatment of simple decompression for lumbar spine with complex LSS can release the pain, but may result in unstability and accelerate the degeneration.

Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data.

BACKGROUND: Major depressive disorder (MDD) has a high rate of recurrence. Identifying patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the disabling effects of depression. METHOD: We propose a novel feature extraction method that includes dynamic temporal information, and inputs the extracted features into a graph convolutional network (GCN) to achieve classification of recurrent MDD. We extract the average time series using an atlas from resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation was calculated between brain region sequences at each time point, representing the functional connectivity at each time point. The connectivity is used as the adjacency matrix and the brain region sequences as node features for a GCN model to classify recurrent MDD. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to analyze the contribution of different brain regions to the model. Brain regions making greater contribution to classification were considered to be the regions with altered brain function in recurrent MDD. RESULT: We achieved a classification accuracy of 75.8 % for recurrent MDD on the multi-site dataset, the Rest-meta-MDD. The brain regions closely related to recurrent MDD have been identified. LIMITATION: The pre-processing stage may affect the final classification performance and harmonizing site differences may improve the classification performance. CONCLUSION: The experimental results demonstrate that the proposed method can effectively classify recurrent MDD and extract dynamic changes of brain activity patterns in recurrent depression.

Three-Dimensional Mapping of Medial Wall in Unstable Pertrochanteric Fractures.

OBJECTIVE: Unstable pertrochanteric fractures are usually treated with internal fixation, and the integrity of the anteromedial cortex is an important factor for stability and healing. In this study, we described and analyzed the three-dimensional mapping technology and morphological characteristics of pertrochanteric fractures. METHODS: Fifty-nine pertrochanteric fractures (OTA/AO 2007 types 31A2) were retrospectively reviewed. Computed tomographic (CT) images for all fractures were superimposed on a standard template. Medial wall integrity was analyzed, and three-dimensional fracture maps were created. RESULTS: Pertrochanteric fractures always have a posterior defect in the medial cortex. The mean width of the defect, in our study, was 21.5 mm (SD: 6.1 mm, range: 10-40 mm), 56.3% (SD: 13.7%, range: 27.5-100%). Bone segments that contact by the anteromedial cortex were 16.5 mm (SD: 5.3 mm, range: 0-29 mm). CONCLUSION: The integrity of the anteromedial cortex should be considered during internal fixation of femoral trochanteric fractures. These morphological characteristics could be used to form postoperative cortical contact and improve stability of the fixation. Three-dimensional mapping technology can help establish a typical fracture model, thereby improving doctors' understanding of fracture characteristics.

3D object reassembly using region-pair-relation and balanced cluster tree.

BACKGROUND AND OBJECTIVE: Object reassembly is a key technology in scenarios such as surgical planning and broken object restoration. Based on previous research, this work intends to explore the general tasks of 3D object reassembly, including conventional object reconstruction and bone fracture reduction. METHODS: We introduce an efficient and robust region-pair-relation descriptor, which incorporates strong geometric constraints and remains invariant to rotation and translation. We segment the fractured objects using balanced cluster tree, and develop a coarse-to-fine method for object reassembly. The matching quality of potential region contact pairs at different depths is estimated recursively from the root of the tree. Once the best contact pairs are determined, the least squares method is implemented to obtain the matching results. In addition, we also provide a semi-interactive manipulation to deal with the complex objects. RESULTS: For most types of broken objects, our approach can generate high accuracy matching results within 10 s, with the cluster tree depth equals to 11. It allows the automatic reassembly of different-sized fragments. For bone fracture blocks with cancellous structures, a semi-interactive operation is integrated so that the precise matching can also be achieved in 30 s. CONCLUSION: The proposed framework can be expanded to various object reassembly tasks in either automated or semi-automated manner, including the fracture reduction problem which used to be an intensive manual process. Therefore, our work shows significant advantages in medical applications.

Synchrotron Radiation-Based Three-Dimensional Visualization of Angioarchitectural Remodeling in Hippocampus of Epileptic Rats.

Characterizing the three-dimensional (3D) morphological alterations of microvessels under both normal and seizure conditions is crucial for a better understanding of epilepsy. However, conventional imaging techniques cannot detect microvessels on micron/sub-micron scales without angiography. In this study, synchrotron radiation (SR)-based X-ray in-line phase-contrast imaging (ILPCI) and quantitative 3D characterization were used to acquire high-resolution, high-contrast images of rat brain tissue under both normal and seizure conditions. The number of blood microvessels was markedly increased on days 1 and 14, but decreased on day 60 after seizures. The surface area, diameter distribution, mean tortuosity, and number of bifurcations and network segments also showed similar trends. These pathological changes were confirmed by histological tests. Thus, SR-based ILPCI provides systematic and detailed views of cerebrovascular anatomy at the micron level without using contrast-enhancing agents. This holds considerable promise for better diagnosis and understanding of the pathogenesis and development of epilepsy.

Implant-bone interface stress distribution in immediately loaded implants of different diameters: a three-dimensional finite element analysis.

PURPOSE: To establish a 3D finite element model of a mandible with dental implants for immediate loading and to analyze stress distribution in bone around implants of different diameters. MATERIALS AND METHODS: Three mandible models, embedded with thread implants (ITI, Straumann, Switzerland) with diameters of 3.3, 4.1, and 4.8 mm, respectively, were developed using CT scanning and self-developed Universal Surgical Integration System software. The von Mises stress and strain of the implant-bone interface were calculated with the ANSYS software when implants were loaded with 150 N vertical or buccolingual forces. RESULTS: When the implants were loaded with vertical force, the von Mises stress concentrated on the mesial and distal surfaces of cortical bone around the neck of implants, with peak values of 25.0, 17.6 and 11.6 MPa for 3.3, 4.1, and 4.8 mm diameters, respectively, while the maximum strains (5854, 4903, 4344 muepsilon) were located on the buccal cancellous bone around the implant bottom and threads of implants. The stress and strain were significantly lower (p < 0.05) with the increased diameter of implant. When the implants were loaded with buccolingual force, the peak von Mises stress values occurred on the buccal surface of cortical bone around the implant neck, with values of 131.1, 78.7, and 68.1 MPa for 3.3, 4.1, and 4.8 mm diameters, respectively, while the maximum strains occurred on the buccal surface of cancellous bone adjacent to the implant neck, with peak values of 14,218, 12,706, and 11,504 microm, respectively. The stress of the 4.1-mm diameter implants was significantly lower (p < 0.05) than those of 3.3-mm diameter implants, but not statistically different from that of the 4.8 mm implant. CONCLUSIONS: With an increase of implant diameter, stress and strain on the implant-bone interfaces significantly decreased, especially when the diameter increased from 3.3 to 4.1 mm. It appears that dental implants of 10 mm in length for immediate loading should be at least 4.1 mm in diameter, and uniaxial loading to dental implants should be avoided or minimized.