Visual Computing for Industry Biomedicine and Art最新文献

Avatars in the educational metaverse are revolutionizing the learning process by providing interactive and effective learning experiences. These avatars enable students to engage in realistic scenarios, work in groups, and develop essential skills using adaptive and intelligent technologies. The purpose of this review is to evaluate the contribution of avatars to education. It investigated the use of avatars to enhance learning by offering individualized experiences and supporting collaborative group activities in virtual environments. It also analyzed the recent progress in artificial intelligence, especially natural language processing and generative models, which have significantly improved avatar capabilities. In addition, it reviewed their use in customized learning, contextual teaching, and virtual simulations to improve student participation and achievement. This study also highlighted issues impacting its implementation, including data security, ethical concerns, and limited infrastructure. The paper ends with implications and recommendations for future research in this field.

Knee osteoarthritis (KOA) is a prevalent chronic condition in the elderly and is often associated with instability caused by anterior cruciate ligament (ACL) degeneration. The functional integrity of ACL is crucial for the diagnosis and treatment of KOA. Radiographic imaging is a practical diagnostic tool for predicting the functional status of the ACL. However, the precision of the current evaluation methodologies remains suboptimal. Consequently, we aimed to identify additional radiographic features from X-ray images that could predict the ACL function in a larger cohort of patients with KOA. A retrospective analysis was conducted on 272 patients whose ACL function was verified intraoperatively between October 2021 and October 2024. The patients were categorized into ACL-functional and ACL-dysfunctional groups. Using least absolute shrinkage and selection operator regression and logistic regression, four significant radiographic predictors were identified: location of the deepest wear on the medial tibial plateau (middle and posterior), wear depth in the posterior third of the medial tibial plateau (> 1.40 mm), posterior tibial slope (PTS > 7.90°), and static anterior tibial translation (> 4.49 mm). A clinical prediction model was developed and visualized using a nomogram with calibration curves and receiver operating characteristic analysis to confirm the model performance. The prediction model demonstrated great discriminative ability, showing area under the curve values of 0.831 (88.4% sensitivity, 63.8% specificity) and 0.907 (86.1% sensitivity, 82.2% specificity) in the training and validation cohorts, respectively. Consequently, the authors established an efficient approach for accurate evaluation of ACL function in KOA patients.

Early allograft dysfunction (EAD) significantly affects liver transplantation prognosis. This study evaluated the effectiveness of artificial intelligence (AI)-assisted methods in accurately diagnosing EAD and identifying its causes. The primary metric for assessing the accuracy was the area under the receiver operating characteristic curve (AUC). Accuracy, sensitivity, and specificity were calculated and analyzed to compare the performance of the AI models with each other and with radiologists. EAD classification followed the criteria established by Olthoff et al. A total of 582 liver transplant patients who underwent transplantation between December 2012 and June 2021 were selected. Among these, 117 patients (mean age 33.5 ± 26.5 years, 80 men) were evaluated. The ultrasound parameters, images, and clinical information of patients were extracted from the database to train the AI model. The AUC for the ultrasound-spectrogram fusion network constructed from four ultrasound images and medical data was 0.968 (95%CI: 0.940, 0.991), outperforming radiologists by 30% for all metrics. AI assistance significantly improved diagnostic accuracy, sensitivity, and specificity (P < 0.050) for both experienced and less-experienced physicians. EAD lacks efficient diagnosis and causation analysis methods. The integration of AI and ultrasound enhances diagnostic accuracy and causation analysis. By modeling only images and data related to blood flow, the AI model effectively analyzed patients with EAD caused by abnormal blood supply. Our model can assist radiologists in reducing judgment discrepancies, potentially benefitting patients with EAD in underdeveloped regions. Furthermore, it enables targeted treatment for those with abnormal blood supply.

Web-based libraries, such as D3.js, ECharts.js, and G6.js, are widely used to generate node-link graph visualizations. These libraries allow users to call application programming interfaces (APIs) without identifying the details of the encapsulated techniques such as graph layout algorithms and graph rendering methods. Efficiency requirements, such as visualizing a graph with 3k nodes and 4k edges within 1 min at a frame rate of 30 fps, are crucial for selecting a proper library because libraries generally present different characteristics owing to the diversity of encapsulated techniques. However, existing studies have mainly focused on verifying the advantages of a new layout algorithm or rendering method from a theoretical viewpoint independent of specific web-based libraries. Their conclusions are difficult for end users to understand and utilize. Therefore, a trial-and-error selection process is required. This study addresses this gap by conducting an empirical experiment to evaluate the performance of web-based libraries. The experiment involves popular libraries and hundreds of graph datasets covering node scales from 100 to 200k and edge-to-node ratios from 1 to 10 (including complete graphs). The experimental results are the time costs and frame rates recorded using the libraries to visualize the datasets. The authors analyze the performance characteristics of each library in depth based on the results and organize the results and findings into application-oriented guidelines. Additionally, they present three usage cases to illustrate how the guidelines can be applied in practice. These guidelines offer user-friendly and reliable recommendations, aiding users in quickly selecting the desired web-based libraries based on their specific efficiency requirements for node-link graph visualizations.

Hypertensive retinopathy (HR) occurs when the choroidal vessels, which form the photosensitive layer at the back of the eye, are injured owing to high blood pressure. Artificial intelligence (AI) in retinal image analysis (RIA) for HR diagnosis involves the use of advanced computational algorithms and machine learning (ML) strategies to recognize and evaluate signs of HR in retinal images automatically. This review aims to advance the field of HR diagnosis by investigating the latest ML and deep learning techniques, and highlighting their efficacy and capability for early diagnosis and intervention. By analyzing recent advancements and emerging trends, this study seeks to inspire further innovation in automated RIA. In this context, AI shows significant potential for enhancing the accuracy, effectiveness, and consistency of HR diagnoses. This will eventually lead to better clinical results by enabling earlier intervention and precise management of the condition. Overall, the integration of AI into RIA represents a considerable step forward in the early identification and treatment of HR, offering substantial benefits to both healthcare providers and patients.

In the task of domain generalization person re-identification (ReID), pedestrian image features exhibit significant intra-class variability and inter-class similarity. Existing methods rely on a single feature extraction architecture and struggle to capture both global context and local spatial information, resulting in weaker generalization to unseen domains. To address this issue, an innovative domain generalization person ReID method-LViT-Net, which combines local semantics and multi-feature cross fusion, is proposed. LViT-Net adopts a dual-branch encoder with a parallel hierarchical structure to extract both local and global discriminative features. In the local branch, the local multi-scale feature fusion module is designed to fuse local feature units at different scales to ensure that the fine-grained local features at various levels are accurately captured, thereby enhancing the robustness of the features. In the global branch, the dual feature cross fusion module fuses local features and global semantic information, focusing on critical semantic information and enabling the mutual refinement and matching of local and global features. This allows the model to achieve a dynamic balance between detailed and holistic information, forming robust feature representations of pedestrians. Extensive experiments demonstrate the effectiveness of LViT-Net. In both single-source and multi-source comparison experiments, the proposed method outperforms existing state-of-the-art methods.

This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering (VQA) systems. The method focuses on identifying false answer predictions by the model and offers users the opportunity to directly correct mistakes in the input space, thus facilitating dataset curation. The decision-making process of the model is demonstrated by highlighting certain internal states of a graph neural network (GNN). The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset. The authors evaluated their tool through the demonstration of identified use cases, quantitative measures, and a user study conducted with experts from machine learning, visualization, and natural language processing domains. The authors' findings highlight the prominence of their implemented features in supporting the users with incorrect prediction identification and identifying the underlying issues. Additionally, their approach is easily extendable to similar models aiming at graph-based question answering.

Breast cancer is one of the most common malignancies among women globally. Magnetic resonance imaging (MRI), as the final non-invasive diagnostic tool before biopsy, provides detailed free-text reports that support clinical decision-making. Therefore, the effective utilization of the information in MRI reports to make reliable decisions is crucial for patient care. This study proposes a novel method for BI-RADS classification using breast MRI reports. Large language models are employed to transform free-text reports into structured reports. Specifically, missing category information (MCI) that is absent in the free-text reports is supplemented by assigning default values to the missing categories in the structured reports. To ensure data privacy, a locally deployed Qwen-Chat model is employed. Furthermore, to enhance the domain-specific adaptability, a knowledge-driven prompt is designed. The Qwen-7B-Chat model is fine-tuned specifically for structuring breast MRI reports. To prevent information loss and enable comprehensive learning of all report details, a fusion strategy is introduced, combining free-text and structured reports to train the classification model. Experimental results show that the proposed BI-RADS classification method outperforms existing report classification methods across multiple evaluation metrics. Furthermore, an external test set from a different hospital is used to validate the robustness of the proposed approach. The proposed structured method surpasses GPT-4o in terms of performance. Ablation experiments confirm that the knowledge-driven prompt, MCI, and the fusion strategy are crucial to the model's performance.

Clamping and removal of the nucleus pulposus (NP) are critical operations during transforaminal endoscopic lumbar discectomy. To meet the challenge of simulating the NP in real-time for better training output, an improved material point method is proposed to represent the physical properties of the NP and compute its deformation in real time. Corresponding volume rendering of the NP and its hosting bones are also presented. The virtual operation procedures are then implemented into a training prototype and subsequently tested through simulation experiments and subjective evaluation. The results have demonstrated the feasibility of the approach.

Federated learning (FL) has shown great potential in addressing data privacy issues in medical image analysis. However, varying data distributions across different sites can create challenges in aggregating client models and achieving good global model performance. In this study, we propose a novel personalized contrastive representation FL framework, named PCRFed, which leverages contrastive representation learning to address the non-independent and identically distributed (non-IID) challenge and dynamically adjusts the distance between local clients and the global model to improve each client's performance without incurring additional communication costs. The proposed weighted model-contrastive loss provides additional regularization for local models, optimizing their respective distributions while effectively utilizing information from all clients to mitigate performance challenges caused by insufficient local data. The PCRFed approach was evaluated on two non-IID medical image segmentation datasets, and the results show that it outperforms several state-of-the-art FL frameworks, achieving higher single-client performance while ensuring privacy preservation and minimal communication costs. Our PCRFed framework can be adapted to various encoder-decoder segmentation network architectures and holds significant potential for advancing the use of FL in real-world medical applications. Based on a multi-center dataset, our framework demonstrates superior overall performance and higher single-client performance, achieving a 2.63% increase in the average Dice score for prostate segmentation.