Objective To review the progress of artificial intelligence (AI) and radiomics in the study of abdominal aortic aneurysm (AAA). Method The literatures related to AI, radiomics and AAA research in recent years were collected and summarized in detail. Results AI and radiomics influenced AAA research and clinical decisions in terms of feature extraction, risk prediction, patient management, simulation of stent-graft deployment, and data mining. Conclusion The application of AI and radiomics provides new ideas for AAA research and clinical decisions, and is expected to suggest personalized treatment and follow-up protocols to guide clinical practice, aiming to achieve precision medicine of AAA.
Cardiovascular diseases are the leading cause of death and their diagnosis and treatment rely heavily on the variety of clinical data. With the advent of the era of medical big data, artificial intelligence (AI) has been widely applied in many aspects such as imaging, diagnosis and prognosis prediction in cardiovascular medicine, providing a new method for accurate diagnosis and treatment. This paper reviews the application of AI in cardiovascular medicine.
With the increasing availability of clinical and biomedical big data, machine learning is being widely used in scientific research and academic papers. It integrates various types of information to predict individual health outcomes. However, deficiencies in reporting key information have gradually emerged. These include issues like data bias, model fairness across different groups, and problems with data quality and applicability. Maintaining predictive accuracy and interpretability in real-world clinical settings is also a challenge. This increases the complexity of safely and effectively applying predictive models to clinical practice. To address these problems, TRIPOD+AI (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence) introduces a reporting standard for machine learning models. It is based on TRIPOD and aims to improve transparency, reproducibility, and health equity. These improvements enhance the quality of machine learning model applications. Currently, research on prediction models based on machine learning is rapidly increasing. To help domestic readers better understand and apply TRIPOD+AI, we provide examples and interpretations. We hope this will support researchers in improving the quality of their reports.
Objective To evaluate the clinical application value of four inflammatory indices [monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR)] in predicting postoperative mortality risk in patients with Siewert type Ⅱ esophagogastric junction adenocarcinoma, and to explore the predictive performance of four inflammatory indices. Methods This retrospective study collected clinical data from 310 patients with Siewert typeⅡ esophagogastric junction adenocarcinoma who were admitted to the Second Hospital of Lanzhou University between October 2016 and March 2023, and met the inclusion and exclusion criteria. Univariate analysis was used to initially screen variables related to postoperative mortality risk. The variance inflation factor (VIF) analysis was performed to assess multicollinearity issues, and multivariate regression analysis was used to further reveal the independent effects of key variables on postoperative mortality risk. The performance of the predictive models was evaluated using receive operatior characteristic curves and Kaplan-Meier survival analysis, and the effects of different inflammatory indices on patient survival were explored. Finally, machine learning methods such as Light GBM, random forest, support vector machine (SVM), and XGBoost were used to evaluate the predictive performance of the four inflammatory indices. Results The four inflammatory indices were significantly associated with postoperative mortality risk in patients with Siewert type Ⅱ esophagogastric junction adenocarcinoma (MLR: HR=2.6884, 95% CI 1.4559 to 4.9642, P=0.002; PLR: HR=1.0022, 95% CI1.0001 to 1.0043, P=0.041; SII: HR=1.0003, 95% CI1.0001 to 1.0006, P=0.002; NLR: HR=1.0697, 95% CI 1.0277 to 1.1134, P=0.001). Machine learning model results showed that NLR had the best performance in the random forest model, with an AUC of 0.863 in the training set and an AUC of 0.834 in the test set. Conclusion Preoperative clinical indicators, especially the NLR inflammatory factor, are of significant importance in predicting the postoperative mortality risk of patients with Siewert typeⅡ esophagogastric junction adenocarcinoma.
Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.
With the development of science and technology, artificial intelligence is gradually integrated into every aspect of daily life and the medical field is no exception. Cardiovascular diseases, as the first killer to global health, is the focus of new technologies and methods. In this study, the application of computer vision, natural language processing, robotics and machine learning in cardiovascular disease studies were reviewed and prospected, in order to promote the development for new technologies and applications in the future.
Wearable devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.
Radiotherapy is one of the main treatments for tumor with increasingly high request for technique precision and the equipment stability. Machine learning may bring radiotherapy simplicity, individualization and precision, and may improve the automatic level of planning and quality assurance. Based on the process of radiotherapy, this paper reviews the applications and researches on machine learning, with an emphasis on deep learning, and proposes the prospects in the following aspects: segmentation of normal tissue and tumor, planning, treatment delivery, quality assurance and prognosis prediction.
Objective To identify genes of lipopolysaccharide (LPS) -induced acute lung injury (ALI) in mice base on bioinformatics and machine learning. Methods The acute lung injury dataset (GSE2411, GSE111241 and GSE18341) were download from the Gene Expression Database (GEO). Differential gene expression analysis was conducted. Gene ontology (GO) analysis, KEGG pathway analysis, GSEA enrichment analysis and protein-protein interaction analysis (PPI) network analysis were performed. LASSO-COX regression analysis and Support Vector Machine Expression Elimination (SVM-RFE) was utilized to identify key biomarkers. Receiver operator characteristic curve was used to evaluate the diagnostic ability. Validation was performed in GSE18341. Finally, CIBERSORT was used to analyze the composition of immune cells, and immunocorrelation analysis of biomarkers was performed. Results A total of 29 intersection DEGs were obtained after the intersection of GSE2411 and GSE111241 differentially expressed genes. Enrichment analysis showed that differential genes were mainly involved in interleukin-17, cytokine - cytokine receptor interaction, tumor necrosis factor and NOD-like receptor signaling pathways. Machine learning combined with PPI identified Gpx2 and Ifi44 were key biomarkers. Gpx2 is a marker of ferroptosis and Ifi44 is an type I interferon-induced protein, both of which are involved in immune regulation. Immunocorrelation analysis showed that Gpx2 and Ifi44 were highly correlated with Neutrophils, TH17 and M1 macrophage cells. Conclusion Gpx2 and Ifi44 have potential immunomodulatory abilities, and may be potential biomarkers for predicting and treating ALI in mince.
The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People’s Republic of China has issued seven trial versions of the “Guidelines for the Diagnosis and Treatment of COVID-19”. However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the “diagnosis and treatment plan” for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of “diagnosis and treatment plan” automatically and intelligently.
