Prediction of pulp exposure risk of carious pulpitis based on deep learning / 华西口腔医学杂志

OBJECTIVES@#This study aims to predict the risk of deep caries exposure in radiographic images based on the convolutional neural network model, compare the prediction results of the network model with those of senior dentists, evaluate the performance of the model for teaching and training stomatological students and young dentists, and assist dentists to clarify treatment plans and conduct good doctor-patient communication before surgery.@*METHODS@#A total of 206 cases of pulpitis caused by deep caries were selected from the Department of Stomatological Hospital of Tianjin Medical University from 2019 to 2022. According to the inclusion and exclusion criteria, 104 cases of pulpitis were exposed during the decaying preparation period and 102 cases of pulpitis were not exposed. The 206 radiographic images collected were randomly divided into three groups according to the proportion: 126 radiographic images in the training set, 40 radiographic images in the validation set, and 40 radiographic images in the test set. Three convolutional neural networks, visual geometry group network (VGG), residual network (ResNet), and dense convolutional network (DenseNet) were selected to analyze the rules of the radiographic images in the training set. The radiographic images of the validation set were used to adjust the super parameters of the network. Finally, 40 radiographic images of the test set were used to evaluate the performance of the three network models. A senior dentist specializing in dental pulp was selected to predict whether the deep caries of 40 radiographic images in the test set were exposed. The gold standard is whether the pulp is exposed after decaying the prepared hole during the clinical operation. The prediction effect of the three network models (VGG, ResNet, and DenseNet) and the senior dentist on the pulp exposure of 40 radiographic images in the test set were compared using receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score to select the best network model.@*RESULTS@#The best network model was DenseNet model, with AUC of 0.97. The AUC values of the ResNet model, VGG model, and the senior dentist were 0.89, 0.78, and 0.87, respectively. Accuracy was not statistically different between the senior dentist (0.850) and the DenseNet model (0.850)(P>0.05). Kappa consistency test showed moderate reliability (Kappa=0.6>0.4, P<0.05).@*CONCLUSIONS@#Among the three convolutional neural network models, the DenseNet model has the best predictive effect on whether deep caries are exposed in imaging. The predictive effect of this model is equivalent to the level of senior dentists specializing in dental pulp.

An EHG-based Preterm Delivery Prediction Algorithm via Convolution Neural Network / 中国医疗器械杂志

Premature delivery is one of the direct factors that affect the early development and safety of infants. Its direct clinical manifestation is the change of uterine contraction intensity and frequency. Uterine Electrohysterography(EHG) signal collected from the abdomen of pregnant women can accurately and effectively reflect the uterine contraction, which has higher clinical application value than invasive monitoring technology such as intrauterine pressure catheter. Therefore, the research of fetal preterm birth recognition algorithm based on EHG is particularly important for perinatal fetal monitoring. We proposed a convolution neural network(CNN) based on EHG fetal preterm birth recognition algorithm, and a deep CNN model was constructed by combining the Gramian angular difference field(GADF) with the transfer learning technology. The structure of the model was optimized using the clinical measured term-preterm EHG database. The classification accuracy of 94.38% and F1 value of 97.11% were achieved. The experimental results showed that the model constructed in this paper has a certain auxiliary diagnostic value for clinical prediction of premature delivery.

A protein complex recognition method based on spatial-temporal graph convolution neural network / 南方医科大学学报

OBJECTIVE@#To propose a new method for mining complexes in dynamic protein network using spatiotemporal convolution neural network.@*METHODS@#The edge strength, node strength and edge existence probability are defined for modeling of the dynamic protein network. Based on the time series information and structure information on the graph, two convolution operators were designed using Hilbert-Huang transform, attention mechanism and residual connection technology to represent and learn the characteristics of the proteins in the network, and the dynamic protein network characteristic map was constructed. Finally, spectral clustering was used to identify the protein complexes.@*RESULTS@#The simulation results on several public biological datasets showed that the F value of the proposed algorithm exceeded 90% on DIP dataset and MIPS dataset. Compared with 4 other recognition algorithms (DPCMNE, GE-CFI, VGAE and NOCD), the proposed algorithm improved the recognition efficiency by 34.5%, 28.7%, 25.4% and 17.6%, respectively.@*CONCLUSION@#The application of deep learning technology can improve the efficiency in analysis of dynamic protein networks.

Heart sound classification based on sub-band envelope and convolution neural network / 生物医学工程学杂志

Automatic classification of heart sounds plays an important role in the early diagnosis of congenital heart disease. A kind of heart sound classification algorithms based on sub-band envelope feature and convolution neural network was proposed in this paper, which did not need to segment the heart sounds according to cardiac cycle accurately. Firstly, the heart sound signal was divided into some frames. Then, the frame level heart sound signal was filtered with Gammatone filter bank to obtain the sub-band signals. Next, the sub-band envelope was extracted by Hilbert transform. After that, the sub-band envelope was stacked into a feature map. Finally, type Ⅰ and type Ⅱ convolution neural network were selected as classifier. The result shown that the sub-band envelope feature was better in type Ⅰ than type Ⅱ. The algorithm is tested with 1 000 heart sound samples. The test results show that the overall performance of the algorithm proposed in this paper is significantly improved compared with other similar algorithms, which provides a new method for automatic classification of congenital heart disease, and speeds up the process of automatic classification of heart sounds applied to the actual screening.

Application of a multi-task learning-based light-weight convolution neural network for the automatic segmentation of organs at risk in thorax / 中华放射肿瘤学杂志

Objective:To evaluate the application of a multi-task learning-based light-weight convolution neural network (MTLW-CNN) for the automatic segmentation of organs at risk (OARs) in thorax.Methods:MTLW-CNN consisted of several layers for sharing features and 3 branches for segmenting 3 OARs. 497 cases with thoracic tumors were collected. Among them, the computed tomography (CT) images encompassing lung, heart and spinal cord were included in this study. The corresponding contours delineated by experienced radiation oncologists were ground truth. All cases were randomly categorized into the training and validation set ( n=300) and test set ( n=197). By applying MTLW-CNN on the test set, the Dice similarity coefficients (DSCs) of 3 OARs, training and testing time and space complexity (S) were calculated and compared with those of Unet and DeepLabv3+ . To evaluate the effect of multi-task learning on the generalization performance of the model, 3 single-task light-weight CNNs (STLW-CNNs) were built. Their structures were totally the same as the corresponding branches in MTLW-CNN. After using the same data and algorithm to train STLW-CNN, the DSCs were statistically compared with MTLW-CNN on the testing set. Results:For MTLW-CNN, the averages (μ) of lung, heart and spinal cord DSCs were 0.954, 0.921 and 0.904, respectively. The differences of μ between MTLW-CNN and other two models (Unet and DeepLabv3+ ) were less than 0.020. The training and testing time of MTLW-CNN were 1/3 to 1/30 of that of Unet and DeepLabv3+ . S of MTLW-CNN was 1/42 of that of Unet and 1/1 220 of that of DeepLabv3+ . The differences of μ and standard deviation (σ) of lung and heart between MTLW-CNN and STLW-CNN were approximately 0.005 and 0.002. The difference of μ of spinal cord was 0.001, but σof STLW-CNN was 0.014 higher than that of MTLW-CNN.Conclusions:MTLW-CNN spends less time and space on high-precision automatic segmentation of thoracic OARs. It can improve the application efficiency and generalization performance of the models.

Automatic segmentation of kidney tumor based on cascaded multiscale convolutional neural networks / 生物医学工程学杂志

The background of abdominal computed tomography (CT) images is complex, and kidney tumors have different shapes, sizes and unclear edges. Consequently, the segmentation methods applying to the whole CT images are often unable to effectively segment the kidney tumors. To solve these problems, this paper proposes a multi-scale network based on cascaded 3D U-Net and DeepLabV3+ for kidney tumor segmentation, which uses atrous convolution feature pyramid to adaptively control receptive field. Through the fusion of high-level and low-level features, the segmented edges of large tumors and the segmentation accuracies of small tumors are effectively improved. A total of 210 CT data published by Kits2019 were used for five-fold cross validation, and 30 CT volume data collected from Suzhou Science and Technology Town Hospital were independently tested by trained segmentation models. The results of five-fold cross validation experiments showed that the Dice coefficient, sensitivity and precision were 0.796 2 ± 0.274 1, 0.824 5 ± 0.276 3, and 0.805 1 ± 0.284 0, respectively. On the external test set, the Dice coefficient, sensitivity and precision were 0.817 2 ± 0.110 0, 0.829 6 ± 0.150 7, and 0.831 8 ± 0.116 8, respectively. The results show a great improvement in the segmentation accuracy compared with other semantic segmentation methods.

A preliminary study of a deep learning assisted diagnostic system with an artificial intelligence for detection of retina disease / 国际眼科杂志(Guoji Yanke Zazhi)

@#AIM:To evaluate the application value of artificial intelligence diagnosis system for fundus disease screening based on deep learning.<p>METHODS:A total of 1 345 patients(2 690 eyes)in our hospital were recruited from July 2018 to December 2018. The accuracy, specificity, consistency and sensitivity of the artificial intelligence diagnosis system were determined by comparison with ophthalmologist diagnosis and artificial intelligence diagnosis system which based on multi-layer deep convolution neural network learning. <p>RESULTS:The accuracy of artificial intelligence diagnosis system is 62.82%. There are 1-5(1.38±0.67)diagnoses among the patients, among which the accuracy of one diagnosis is 56.09%, the accuracy of two diagnosis is 77.96%, the accuracy of three diagnosis is 84.61%, the accuracy of four diagnosis is 86.95%, and the accuracy of five diagnosis is 60.00%; The consistency kappa value without obvious abnormality and leopard pattern fundus was 0.044 and 0.169 respectively. The sensitivity was 3.00% and 99.6% respectively, the specificity was 99.7% and 14.2% respectively. The consistency Kappa value of other diagnosis was as high as 0.57-1.00, the sensitivity was as high as 65.1%-100%, and the specificity was as high as 93.0%-100%. <p>CONCLUSION:This study shows that the artificial intelligence diagnosis system based on multi-layer deep convolution neural network learning is a reliable alternative to diagnose retina diseases, and it is expected to become an effective screening tool for primary medical treatment.

Heartbeat-based end-to-end classification of arrhythmias / 南方医科大学学报

OBJECTIVE@#We propose a heartbeat-based end-to-end classification of arrhythmias to improve the classification performance for supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB).@*METHODS@#The ECG signals were preprocessed by heartbeat segmentation and heartbeat alignment. An arrhythmia classifier was constructed based on convolutional neural network, and the proposed loss function was used to train the classifier.@*RESULTS@#The proposed algorithm was verified on MIT-BIH arrhythmia database. The AUC of the proposed loss function for SVEB and VEB reached 0.77 and 0.98, respectively. With the first 5 min segment as the local data, the diagnostic sensitivities for SVEB and VEB were 78.28% and 98.88%, respectively; when 0, 50, 100, and 150 samples were used as the local data, the diagnostic sensitivities for SVEB and VEB reached 82.25% and 93.23%, respectively.@*CONCLUSIONS@#The proposed method effectively reduces the negative impact of class-imbalance and improves the diagnostic sensitivities for SVEB and VEB, and thus provides a new solution for automatic arrhythmia classification.

Method on automatic location of inserts in electron density phantom / 中国医学影像技术

Objective: To investigate automatic location of inserts in the electron density phantom (CIRS 062) based on deep neural network (DCNN). Methods Firstly, four inserts in CIRS 062 were segmented with DCNN model, namely the inhaled lung, the exhaled lung, the solid trabecular bone and the solid dense bone. Then Moore-neighbor tracking algorithm was used to process the segmentation results to obtain the precise segmentation edges. Finally, the other four inserts were located based on the geometric features. Results The results of Dice similarity coefficient were all >0.85, the precision were all >0.81, and F1-measure were all >0.61 based on DCNN. Conclusion The method based on DCNN can realize the automatic positioning of the inserts.

Research progress of artificial intelligence convolutional neural network in whole slide image analysis / 中国胸心血管外科临床杂志

@#Histopathology is still the golden standard for the diagnosis of clinical diseases. Whole slide image (WSI) can make up for the shortcomings of traditional glass slices, such as easy damage, difficult retrieval and poor diagnostic repeatability, but it also brings huge workload. Artificial intelligence (AI) assisted pathologist's WSI analysis can solve the problem of low efficiency and improve the consistency of diagnosis. Among them, the convolution neural network (CNN) algorithm is the most widely used. This article aims to review the reported application of CNN in WSI image analysis, summarizes the development trend of CNN in the field of pathology and makes a prospect.

A review on brain age prediction in brain ageing / 生物医学工程学杂志

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.

Development of a Deep Learning Algorithm for Classification of Neuroblastoma / 中国医疗器械杂志

In this paper, the classification and location of neuroblastoma in NMR images are realized by using Deep Neural Network(CNN) algorithm as the core technology. The module is integrated to realize the development of computer-aided diagnostic software. It is used to make up for the gap in the field of intelligent identification and accurate positioning of neuroblastoma in the current nuclear magnetic resonance detection technology, effectively reduce the work intensity of doctors reading films, and further promote the clinical application and technical development of nuclear magnetic resonance detection technology in the diagnosis of neuroblastoma.

Detection of pulmonary nodules based on conventional neural networks / 中国医学影像技术

Objective Major challenges in the current automatic detection of lung nodules from chest CT images are to improve the sensitivity and to reduce the false positive rate.A new scheme based on convolutional neural network was proposed in this study.Methods The method applied an automatic anatomy recognition (AAR) methodology based on fuzzy modeling ideas and an iterative relative fuzzy connectedness (IRFC) delineation algorithm for the segmentation of lung parenchyma in CT images.The segmented lung image was inputted into the conventional neural networks for feature extraction of pulmonary nodules.The network adopted position-sensitive score maps to express the location information of lung nodules.Results This method could obtain accurate segmentation of the lung parenchyma in the data set of Tianchi Medical AI Contest,and the accuracy,sensitivity,specificity and false-positive rate of lung nodules detected was 95.60 ％,95.24％,95.97％ and 4.03％,respectively.Conclusion Detection of pulmonary nodules based on convolutional neural networks has high accuracy and efficiency,and good robustness.