ObjectiveTo establish a model for predicting microvascular invasion (MVI) of hepatocellular carcinoma based on magnetic resonance imaging (MRI) radiomics features.MethodsThe clinical and pathological datas of 190 patients with hepatocellular carcinoma who received surgical treatment in our hospital from September 2017 to May 2020 were prospectively collected. The patients were randomly divided into training group (n=158) and test group (n=32) with a ratio of 5∶1. Gadoxetate disodium (Gd-EOB-DTPA) -enhanced MR images of arterial phase and hepatobiliary phase were used to select radiomics features through the region of interest (ROI). The ROI included the tumor lesions and the area dilating to 2 cm from the margin of the tumor. Based on a machine learning algorithm logistic, a radiomics model for predicting MVI of hepatocellular carcinoma was established in the training group, and the model was evaluated in the test group.ResultsSeven radiomics features were obtained. The area under the receiver operating characteristic curve (AUC) of the training group and the test group were 0.830 [95%CI (0.669, 0.811)] and 0.734 [95%CI (0.600, 0.936)], respectively.ConclusionThe model based on MRI radiomics features seems to be a promising approach for predicting the microvascular invasion of hepatocellular carcinoma, which is of clinical significance for the management of hepatocellular carcinoma treatment.
Objective To summarize advances in the application of machine learning in the diagnosis and treatment of liver disease. Method The recent literatures on the progress of machine learning in the diagnosis, treatment and prognosis of liver diseases were reviewed. Results Machine learning could be used to diagnose and categorize substantial liver lesions, tumourous lesions and rare liver diseases at an early stage, which could facilitate clinicians to take timely and appropriate treatment measures. Machine learning was helpful in informing clinicians in choosing the best treatment decision, which was conducive to reducing medical risks. It could also help to determine the prognosis of patients in a comprehensive manner, and provide assistance in formulating early rehabilitation treatment plans, adjusting follow-up strategies and improving future prognosis. Conclusions Multiple types of machine learning algorithms have achieved positive results in the clinical application of liver diseases by constructing different prediction models, and have great potential and excellent prospects in multiple aspects such as diagnosis, treatment and prognosis of liver diseases.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.
Objective To review the application and research progress of artificial intelligence (AI) technology in trauma treatment. MethodsThe recent research literature on the application of AI and related technologies in trauma treatment was reviewed and summarized in terms of prehospital assistance, in-hospital emergency care, and post-traumatic stress disorder risk regression prediction, meanwhile, the development trend of AI technology in trauma treatment were outlooked. Results The AI technology can rapidly analyze and manage large amount of clinical data to help doctors identify patients’ situation of trauma and predict the risk of possible complications more accurately. The application of AI technology in surgical assistance and robotic operations can achieve precise surgical plan and treatment, reduce surgical risks, and shorten the operation time, so as to improve the efficiency and long-term effectiveness of the trauma treatment. ConclusionThere is a promising future for the application of AI technology in the trauma treatment. However, it is still in the stage of exploration and development, and there are many difficulties of historical data bias, application condition limitations, as well as ethical and moral issues need to be solved.
Artificial intelligence belongs to the field of computer science. In the past few decades, artificial intelligence has shown broad application prospects in the medical field. With the development of computer technology in recent years, doctors and computer scientists have just begun to discover its potential for clinical application, especially in the field of congenital heart disease. Artificial intelligence now has been successfully applied to the prediction, intelligent diagnosis, medical image segmentation and recognition, clinical decision support of congenital heart disease. This article reviews the application of artificial intelligence in congenital cardiology.
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.
ObjectiveTo explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). MethodsThe clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). ResultsFour radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. ConclusionThe machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.
ObjectiveTo review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. MethodsA comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. ResultsThe rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. ConclusionThe expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
Objective To identify the N6-methyladenosine (m6A)-related characteristic genes analyzed by gene clustering and immune cell infiltration in myocardial ischemia-reperfusion injury (MI/RI) after cardiopulmonary bypass through machine learning. Methods The differential genes associated with m6A methylation were screened by the dataset GSE132176 in GEO, the samples of the dataset were clustered based on the differential gene expression profile, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differential genes of the m6A cluster after clustering were performed to determine the gene function of the m6A cluster. R software was used to determine the better models in machine learning of support vector machine (SVM) model and random forest (RF) model, which were used to screen m6A-related characteristic genes in MI/RI, and construct characteristic gene nomogram to predict the incidence of disease. R software was used to analyze the correlation between characteristic genes and immune cells, and the online website was used to build a characteristic gene regulatory network. Results In this dataset, a total of 5 m6A-related differential genes were screened, and the gene expression profiles were divided into two clusters for cluster analysis. The enrichment analysis of m6A clusters showed that these genes were mainly involved in regulating monocytes differentiation, response to lipopolysaccharides, response to bacteria-derived molecules, cellular response to decreased oxygen levels, DNA transcription factor binding, DNA-binding transcription activator activity, RNA polymerase Ⅱ specificity, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, tumor necrosis factor signaling pathway, interleukin-17 signaling pathway. The RF model was determined by R software as the better model, which determined that METTL3, YTHDF1, RBM15B and METTL14 were characteristic genes of MI/RI, and mast cells, type 1 helper lymphocytes (Th1), type 17 helper lymphocytes (Th17), and macrophages were found to be associated with MI/RI after cardiopulmonary bypass in immune cell infiltration. Conclusion The four characteristic genes METTL3, YTHDF1, RBM15B and METTL14 are obtained by machine learning, while cluster analysis and immune cell infiltration analysis can better reveal the pathophysiological process of MI/RI.
Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.