Computers & Graphics celebrates its golden anniversary in 2025. Motivated by this special event, this study presents a comprehensive bibliometric analysis of the journal, identifying key research trends, frequently cited authors, institutions, countries, and major citation patterns. The work retrieves data from the Web of Science (WoS) core collection and Scopus databases and utilizes bibliometric tools such as VOS viewer and bibliometrix software. We analyse the keyword evolution; co-citation networks and bibliographic coupling of the documents published in Computers & Graphics . The distribution of topics indicates increased attention to artificial intelligence–based methods, including deep learning, point cloud processing, and virtual reality, alongside established rendering and simulation techniques. Additionally, the bibliometric analysis of productive authors, institutions and countries, indicate increased publication and citation activity associated with institutions in Asian countries, especially China. Beyond broader trends, this study also highlights Computers & Graphics’ recent initiatives that emphasize transparency and reproducibility, such as the graphics replicability stamp and the special sections, which bridge academic conferences and high-quality journal publications. This study serves as a reference for researchers seeking to understand the historical trajectory, emerging trends, and evolving editorial priorities in computer graphics research.

Abstract Traditional regression-based approaches do not provide good results in diagnosis and prediction of occurrences of cardiovascular diseases (CVD). Therefore, the goal of this paper is to propose a deep learning–based prediction model of occurrence of major adverse cardiac events (MACE) during the 1, 6, 12 month follow-up after hospital admission in acute myocardial infarction (AMI) patients using knowledge mining. We used the Korea Acute Myocardial Infarction Registry (KAMIR) dataset, a cardiovascular disease database registered in 52 hospitals in Korea between 1 January, 2005, and 31 December, 2008. Among 14,885 AMI patients, 10,813 subjects in age from 20 to 100 years with the 1-year follow-up traceability without coding errors were finally selected. For our experiment, the training/validation/test dataset split is 60/20/20 by random sampling without replacement. The preliminary deep learning model was first built by applying training and validation datasets and then a new preliminary deep learning model was generated using the best hyperparameters obtained from random hyperparameter grid search. Lastly, the preliminary prediction model of MACE occurrences in AMI patients is evaluated by test dataset. Compared with conventional regression-based models, the performances of machine/deep learning–based prediction models of the MACE occurrence in patients with AMI, including deep neural network (DNN), gradient boosting machine (GBM), and generalized linear model (GLM), are also evaluated through a matrix with sensitivity, specificity, overall accuracy, and the area under the ROC curve (AUC). The prediction results of the MACE occurrence during the 1, 6, and 12-month follow-up in AMI patients were the AUC of DNN (1 M 0.97, 6 M 0.94, 12 M 0.96), GBM (0.96, 0.95, 0.96), and GLM (0.76, 0.67, 0.72) in machine learning–based models as well as GRACE (0.75, 0.72, 0.76) in regression model. Compared with previous models, our deep learning–based prediction models significantly had the accuracy of 95% or higher and outperformed all machine learning and regression-based prediction models. This paper was the first trial of deep learning–based prediction model of the MACE occurrence in AMI clinical data. We found that the proposed prediction model applied different risk factors except the attribute “age” by using knowledge mining and directly used the raw data as input.