OBJECTIVES: To design a deep learning model that balances model complexity and performance to enable its integration into wearable ECG monitoring devices for automated diagnosis of atrial fibrillation. METHODS: This study was performed based on data from 84 patients with atrial fibrillation, 25 patients with atrial fibrillation, and 18 subjects without obvious arrhythmia collected from the publicly available datasets LTAFDB, AFDB, and NSRDB, respectively. A lightweight attention network based on depthwise separable convolution and fusion of channel-spatial information, namely DSC-AttNet, was proposed. Depthwise separable convolution was introduced to replace standard convolution and reduce model parameters and computational complexity to realize high efficiency and light weight of the model. The multilayer hybrid attention mechanism was embedded to compute the attentional weights of the channels and spatial information at different scales to improve the feature expression ability of the model. Ten-fold cross-validation was performed on LTAFDB, and external independent testing was conducted on AFDB and NSRDB datasets. RESULTS: DSC-AttNet achieved a ten-fold average accuracy of 97.33% and a precision of 97.30% on the test set, both of which outperformed the other 4 comparison models as well as the 3 classical models. The accuracy of the model on the external test set reached 92.78%, better than those of the 3 classical models. The number of parameters of DSC-AttNet was 1.01M, and the computational volume was 27.19G, both smaller than the 3 classical models. CONCLUSIONS This proposed method has a smaller complexity, achieves better classification performance, and has a better generalization ability for atrial fibrillation classification.
Atrial Fibrillation/diagnosis* ; Humans ; Electrocardiography ; Deep Learning ; Wearable Electronic Devices ; Neural Networks, Computer

OBJECTIVES: We propose a myocardial infarction (MI) detection and localization model for improving the diagnostic accuracy for MI to provide assistance to clinical decision-making. METHODS: The proposed model was constructed based on multi-scale field residual blocks fusion modified channel attention (MSF-RB-MCA). The model utilizes lead II electrocardiogram (ECG) signals to detect and localize MI, and extracts different levels of feature information through the multi-scale field residual block. A modified channel attention for automatic adjustment of the feature weights was introduced to enhance the model's ability to focus on the MI region, thereby improving the accuracy of MI detection and localization. RESULTS: A 5-fold cross-validation test of the model was performed using the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset. For MI detection, the model achieved an accuracy of 99.96% on the test set with a specificity of 99.84% and a sensitivity of 99.99%. For MI localization, the accuracy, specificity and sensitivity were 99.81%, 99.98% and 99.65%, respectively. The performances of the model for MI detection and localization were superior to those of other comparison models. CONCLUSIONS The proposed MSF-RB-MCA model shows excellent performance in AI detection and localization based on lead II ECG signals, demonstrating its great potential for application in wearable devices.
Myocardial Infarction/diagnosis* ; Humans ; Electrocardiography/methods* ; Signal Processing, Computer-Assisted ; Algorithms ; Sensitivity and Specificity

Objective To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.Methods We proposed a two-stage carotid plaque detection method based on deep convolutional neural network(SM-YOLO).A series of algorithms such as median filtering,histogram equalization,and Gamma transformation were used to preprocess the dataset to improve image quality.In the first stage of the model construction,a candidate plaque set was built based on the YOLOX_l target detection network,using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes.In the second stage,the Histogram of Oriented Gradient(HOG)features and Local Binary Pattern(LBP)features were extracted and fused,and a Support Vector Machine(SVM)classifier was used to screen the candidate plaque set to obtain the final detection results.This model was compared quantitatively and visually with several target detection models(YOLOX_l,SSD,EfficientDet,YOLOV5_l,Faster R-CNN).Results SM-YOLO achieved a recall of 89.44%,an accuracy of 90.96%,a F1-Score of 90.19%,and an AP of 92.70%on the test set,outperforming other models in all performance indicators and visual effects.The constructed model had a much shorter detection time than the Faster R-CNN model(only one third of that of the latter),thus meeting the requirements of real-time detection.Conclusion The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.

Objective To develop a method for accurate identification of multiscale carotid plaques in ultrasound images.Methods We proposed a two-stage carotid plaque detection method based on deep convolutional neural network(SM-YOLO).A series of algorithms such as median filtering,histogram equalization,and Gamma transformation were used to preprocess the dataset to improve image quality.In the first stage of the model construction,a candidate plaque set was built based on the YOLOX_l target detection network,using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes.In the second stage,the Histogram of Oriented Gradient(HOG)features and Local Binary Pattern(LBP)features were extracted and fused,and a Support Vector Machine(SVM)classifier was used to screen the candidate plaque set to obtain the final detection results.This model was compared quantitatively and visually with several target detection models(YOLOX_l,SSD,EfficientDet,YOLOV5_l,Faster R-CNN).Results SM-YOLO achieved a recall of 89.44%,an accuracy of 90.96%,a F1-Score of 90.19%,and an AP of 92.70%on the test set,outperforming other models in all performance indicators and visual effects.The constructed model had a much shorter detection time than the Faster R-CNN model(only one third of that of the latter),thus meeting the requirements of real-time detection.Conclusion The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

Objective To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging(MRI)deep learning features with clinical features for preoperative prediction of cytokeratin 19(CK19)status of hepatocellular carcinoma(HCC).Methods A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status.A single sequence multi-scale feature fusion deep learning model(MSFF-IResnet)and a multi-scale and multi-modality feature fusion model(MMFF-IResnet)were established based on the hepatobiliary phase(HBP),diffusion weighted imaging(DWI)sequences of enhanced MRI images,and the clinical features significantly correlated with CK19 status.The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery.Results Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio(P=0.029)and incomplete tumor capsule(P=0.028)were independent predictors of CK19 expression in HCC.The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models,and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%,an accuracy of 80.6%,a sensitivity of 80.1%and a specificity of 81.2%.Conclusion The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC,demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.

OBJECTIVE: To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction. METHODS: This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T₁WI, T₂WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques. RESULTS: The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI. CONCLUSION The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.
Humans ; Carcinoma, Hepatocellular ; Retrospective Studies ; Liver Neoplasms ; Magnetic Resonance Imaging ; Algorithms

OBJECTIVE To evaluate the effectiveness， safety， economy， innovation， suitability and accessibility of recombinant Mycobacterium tuberculosis fusion protein （EC）， and to provide evidence for selecting skin detection methods for tuberculosis infection diagnosis and auxiliary diagnosis of tuberculosis. METHODS The effectiveness and safety of EC compared with purified protein derivative of tuberculin （TB-PPD） were analyzed by the method of systematic review. Cost minimization analysis， cost-effectiveness analysis and cost-utility analysis were used to evaluate the short-term economy of EC compared with TB-PPD， and cost-utility analysis was used to evaluate the long-term economy. The evaluation dimensions of innovation， suitability and accessibility were determined by systematic review and improved Delphi expert consultation， and the comprehensive score of EC and TB-PPD in each dimension were calculated by the weight of each indicator. RESULTS The scores of effectiveness， safety， economy， innovation and suitability of EC were all higher than those of TB-PPD. The affordability scores of the two drugs were consistent， while the availability score of EC was lower than those of TB-PPD. After considering dimensions and index weight， the scores of effectiveness， safety， economy， innovation， suitability， accessibility and the comprehensive score of EC were all higher than those of TB-PPD. CONCLUSIONS Compared with TB-PPD， EC performs better in all dimensions of effectiveness， safety， economy， innovation， suitability and accessibility. However， it is worth noting that EC should further improve its availability in the dimension of accessibility.

Objective:To explore the effect of setting up an internal-cross disciplinary team (ICDT) in the intensive care unit (ICU) on a new model of overall treatment for patients with chronic critical illness (CCI).Methods:A 60-year-old male patient with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) admitted to ICU in the Second Affiliated Hospital of Fujian Medical University was introduced. The role of ICDT composed of physicians, nurses, respiratory therapists, physiotherapists, clinical dietitians and patients' family members in ventilator withdrawal and super-early rehabilitation was analyzed in this case.Results:The patient was diagnosed as AECOPD, type Ⅱ aspiration penumonia respiratory failure, septic shock. The ICDT in ICU carried out early rehabilitation treatment for the patient on the basis of traditional infection control and supportive treatment. Under the care of the ICDT, peripheral blood white blood cell count (WBC), neutrophil count (NEU), procalcitonin (PCT), arterial partial pressure of carbon dioxide (PaCO 2), maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP), right excursion of diaphragm, sputum viscosity, tidal volume (VT) and respiratory rate (RR) were improved. Subsequently, the ventilator mode was gradually changed and the ventilator parameters were down-regulated. The ventilator was successfully weaned on the 10th day of treatment. After weaning, the patient's bedside pulmonary function indicators improved, and he was transferred out of ICU on the 15th day of treatment and discharged on the 20th day. The mental state of the patients was good and the quality of life was greatly improved in CCI outpatient follow-up. Conclusions:ICDT cooperation is very important for monitoring and treatment of CCI patients, which is beneficial to the super-early rehabilitation and prognosis improvement of critically ill patients.

Antipyretic-analgesics are currently one of the most prescribed drugs in children.The clinical application of antipyretic-analgesics for children in our country still have irrational phenomenon, which affects the therapeutic effect and even poses hidden dangers to the safety of children.In this paper, suggestions were put forward from the indications, dosage form/route, dosage suitability, pathophysiological characteristics of children with individual differences and drug interactions in the symptomatic treatment of febrile children, so as to provide reference for the general pharmacists when conducting prescription review.