Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis.

ABSTRACT: Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.

A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues based on animal experiments.

The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation.

A preliminary study of calcium channel-associated mRNA and miRNA networks in post-traumatic epileptic rats.

The calcium channels are the main pathogenesis and therapeutic target for post-traumatic epilepsy (PTE). However, differentially expressed miRNAs (DEMs) and mRNAs associated with calcium channels in PTE and their interactions are poorly understood. We produced a PTE model in rats and conducted RNA-seq in PTE rats. Gene annotation was used to verify differentially expressed mRNAs related to calcium channels. RNAhybrid, PITA, and Miranda prediction were used to build the miRNA-mRNA pairs. Furthermore, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used for the functional enrichment analysis of DEMs. The quantification changes of mRNA and miRNA were verified by RT-qPCR. There were 431 identified differentially expressed genes (DEGs) in PTE rats compared with the sham group, of which five mRNAs and 7 miRNAs were related to calcium channels. The miRNA-mRNA network suggested a negative correlation between 11 pairs of miRNA-mRNA involved in the p53 signaling pathway, HIF-1 signaling pathway. RT-qPCR verified three upregulated mRNAs in PTE rats, associated with 7 DEMs negatively related to them, respectively. This study has revealed the changes in miRNA-mRNA pairs associated with calcium channels in PTE, which might contribute to the further interpretation of potential underlying molecular mechanisms of PTE and the discovery of promising diagnostics.

Mapping the knowledge of traffic collision Reconstruction: A scientometric analysis in CiteSpace, VOSviewer, and SciMAT.

Traffic collisions are incidents with high fatality rate which generate billions of US dollars of loss worldwide each year. Post-collision scene reconstruction, which involves knowledge of multiple disciplines, is an important approach to restore the traffic collision and infer the cause of it. This paper uses software CiteSpace, VOSviewer, and SciMAT to conduct a visualization study of knowledge mapping on the literature of traffic collision scene reconstruction from 2001 to 2021 based on the Web of Science database. Knowledge mapping is a cutting-edge research method in scientometric, which has been widely applied in medicine and informatics. Compared with traditional literature review, knowledge mapping with visual techniques identifies hot keywords and key literature in the field more scientifically, and displays them in schematic diagrams intuitively which allows to further predict potential hotspots. A total of 803 original papers are retrieved to analyze and discuss the evolution of the field in the past 20 years, from macro to micro, in term of background information, popular themes, and knowledge structure. Results indicate the number of publications in this field is limited, and collaborations among authors and among institutions are insufficient. In the meantime, mappings imply the top three hot themes being scene reconstruction, computer technology, and injuries. The introduction of AI related technologies, such as neural networks and genetic algorithms, into collision reconstruction would be a potential research direction.

Mapping the Knowledge of Antipsychotics-Induced Sudden Cardiac Death: A Scientometric Analysis in CiteSpace and VOSviewer.

The drugs on the market for schizophrenia are first-generation and second-generation antipsychotics. Some of the first-generation drugs have more side effects than the other drugs, so they are gradually no longer being applied clinically. Years of research have shown that the risk of sudden cardiac death in psychotic patients is associated with drug use, and antipsychotic drugs have certain cardiotoxicity and can induce arrhythmias. The mechanism of antipsychotic-induced sudden cardiac death is complicated. Highly cited papers are among the most commonly used indicators for measuring scientific excellence. This article presents a high-level analysis of highly cited papers using Web of Science core collection databases, scientometrics methods, and thematic clusters. Temporal dynamics of focus topics are identified using a collaborative network (author, institution, thematic clusters, and temporal dynamics of focus topics are identified), keyword co-occurrence analysis, co-citation clustering, and keyword evolution. The primary purpose of this study is to discuss the visual results, summarize the research progress, and predict the future research trends by bibliometric methods of CiteSpace and VOSviewer. This study showed that a research hotspot is that the mechanisms of cardiotoxicity, the safety monitoring, and the assessment of the risk-benefit during clinical use of some newer antipsychotics, clozapine and olanzapine. We discussed relevant key articles briefly and provided ideas for future research directions for more researchers to conduct related research.

Wavelet geographically weighted regression for spectroscopic modelling of soil properties.

Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible-near infrared (vis-NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares regression (PLSR), and machine learning are the most common methods for modelling soil properties with spectra. Often, such models do not account for the multiresolution information presented in the vis-NIR signal, or the spatial variation in the data. To address these potential shortcomings, we used wavelets to decompose the vis-NIR spectra of 226 soils from agricultural and forested regions in south-western Western Australia and developed a wavelet geographically weighted regression (WGWR) for estimating soil organic carbon content, clay content and pH. To evaluate the WGWR models, we compared them to linear models derived with multiresolution data from a wavelet decomposition (WLR) and PLSR without multiresolution information. Overall, validation of the WGWR models produced more accurate estimates of the soil properties than WLR and PLSR. Around 3.5-49.1% of the improvement in the estimates was due to the multiresolution analysis and 1.0-5.2% due to the integration of spatial information in the modelling. The WGWR improves the modelling of soil properties with spectra.

Automated spectroscopic modelling with optimised convolutional neural networks.

Convolutional neural networks (CNN) for spectroscopic modelling are currently tuned manually, and the effects of their hyperparameters are not analysed. These can result in sub-optimal models. Here, we propose an approach to tune one-dimensional CNN (1D-CNNs) automatically. It consists of a parametric representation of 1D-CNNs and an optimisation of hyperparameters to maximise a model's performance. We used a large European soil spectroscopic database to demonstrate our approach for estimating soil organic carbon (SOC) contents. To assess the optimisation, we compared it to random search, and to understand the effects of the hyperparameters, we calculated their importance using functional Analysis of Variance. Compared to random search, the optimisation produced better final results and showed faster convergence. The optimal model produced the most accurate estimates of SOC with [Formula: see text] (s.d.) and [Formula: see text] (s.d.). The hyperparameters associated with model training and architecture critically affected the model's performance, while those related to the spectral preprocessing had little effect. The optimisation searched through a complex hyperparameter space and returned an optimal 1D-CNN. Our approach simplified the development of 1D-CNNs for spectroscopic modelling by automatically selecting hyperparameters and preprocessing methods. Hyperparameter importance analysis shed light on the tuning process and increased the model's reliability.