ObjectiveTo evaluate the use of machine learning algorithms for the prediction and characterization of cardiac thrombosis in patients with valvular heart disease and atrial fibrillation. MethodsThis article collected data of patients with valvular disease and atrial fibrillation from West China Hospital of Sichuan University and its branches from 2016 to 2021. From a total of 2 515 patients who underwent valve surgery, 886 patients with valvular disease and atrial fibrillation were included in the study, including 545 (61.5%) males and 341 (38.5%) females, with a mean age of 55.62±9.26 years, and 192 patients had intraoperatively confirmed cardiac thrombosis. We used five supervised machine learning algorithms to predict thrombosis in patients. Based on the clinical data of the patients (33 features after feature screening), the 10-fold nested cross-validation method was used to evaluate the predictive effect of the model through evaluation indicators such as area under the curve, F1 score and Matthews correlation coefficient. Finally, the SHAP interpretation method was used to interpret the model, and the characteristics of the model were analyzed using a patient as an example. ResultsThe final experiment showed that the random forest classifier had the best comprehensive evaluation indicators, the area under the receiver operating characteristic curve was 0.748±0.043, and the accuracy rate reached 79.2%. Interpretation and analysis of the model showed that factors such as stroke volume, peak mitral E-wave velocity and tricuspid pressure gradient were important factors influencing the prediction. ConclusionThe random forest model achieves the best predictive performance and is expected to be used by clinicians as an aided decision-making tool for screening high-embolic risk patients with valvular atrial fibrillation.
ObjectiveTo use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. MethodsA total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. ResultsThe voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. ConclusionMachine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.
Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.
ObjectiveTo construct a multimodal imaging radiomics model based on enhanced CT features to predict tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (NCRT). MethodsA retrospective analysis was conducted on the Database from Colorectal Cancer (DACCA) at West China Hospital of Sichuan University, including 199 LARC patients treated from October 2016 to October 2023. All patients underwent total mesorectal excision after NCRT. Clinical pathological information was collected, and radiomics features were extracted from CT images prior to NCRT. Python 3.13.0 was used for feature dimension reduction, and univariate logistic regression (LR) along with Lasso regression with 5-fold cross-validation were applied to select radiomics features. Patients were randomly divided into training and testing sets at a ratio of 7∶3 for machine learning and joint model construction. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). Receiver operating characteristic curve (ROC), confusion matrices, and clinical decision curves (DCA) were plotted to assess the model’s performance. ResultsAmong the 199 patients, 155 (77.89%) had poor therapeutic outcomes, while 44 (22.11%) had good outcomes. Univariate LR and Lasso regression identified 8 clinical pathological features and 5 radiomic features, including 1 shape feature, 2 first-order statistical features, and 2 texture features. LR, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models were 0.99, 0.98, 1.00, and 1.00, respectively, with accuracy rates of 0.94, 0.93, 1.00, and 1.00, sensitivity rates of 0.98, 1.00, 1.00, and 1.00, and specificity rates of 0.80, 0.67, 1.00, and 1.00, respectively. In the testing set, the AUC values of 4 models were 0.97, 0.92, 0.96, and 0.95, with accuracy rates of 0.87, 0.87, 0.88, and 0.90, sensitivity rates of 1.00, 1.00, 1.00, and 0.95, and specificity rates of 0.50, 0.50, 0.56, and 0.75. Among the models, the XGBoost model had the best performance, with the highest accuracy and specificity rates. DCA indicated clinical benefits for all 4 models. ConclusionsThe multimodal imaging radiomics model based on enhanced CT has good clinical application value in predicting the efficacy of NCRT in LARC. It can accurately predict good and poor therapeutic outcomes, providing personalized clinical surgical interventions.
With the advancement and development of computer technology, the medical decision-making system based on artificial intelligence (AI) has been widely applied in clinical practice. In the perioperative period of cardiovascular surgery, AI can be applied to preoperative diagnosis, intraoperative, and postoperative risk management. This article introduces the application and development of AI during the perioperative period of cardiovascular surgery, including preoperative auxiliary diagnosis, intraoperative risk management, postoperative management, and full process auxiliary decision-making management. At the same time, it explores the challenges and limitations of the application of AI and looks forward to the future development direction.
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
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 construct a radiomics model for identifying clinical high-risk carotid plaques. Methods A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. ResultsFinally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.
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.
With the innovation and breakthrough of key technologies in smart medicine, actively exploring smart emergency measures and methods with artificial intelligence as the core technology is helpful to improve the ability of emergency medical team to diagnose and treat acute and critical diseases. This paper reviews the application status of artificial intelligence in pre-hospital and in-hospital diagnosis and treatment capabilities and system construction, expounds on the challenges it faces and possible coping strategies, and provides a reference for the in-depth integration and development of “artificial intelligence + emergency medicine” education, research and production during the new wave of scientific and technological revolution.