Automated Recognition of Ultrasound Cardiac Views Based on Deep Learning with Graph Constraint

In transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of cardiac views of the TTE examination, particularly when obtained by non-trained providers. A new method for automatic recognition of cardiac views is proposed consisting of three processes. First, a spatial transform network is performed to learn cardiac shape changes during a cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrate channel-wise feature responses. Finally, the structured signals by the similarities among cardiac views are transformed into the graph-based image embedding, which acts as unsupervised regularization constraints to improve the generalization accuracy. The proposed method is trained and tested in 171792 cardiac images from 584 subjects. The overall accuracy of the proposed method on cardiac image classification is 99.10%, and the mean AUC is 99.36%, better than known methods. Moreover, the overall accuracy is 97.73%, and the mean AUC is 98.59% on an independent test set with 37,883 images from 100 subjects. The proposed automated recognition model achieved comparable accuracy with true cardiac views, and thus can be applied clinically to help find standard cardiac views.

A Novel Multiway Splits Decision Tree for Multiple Types of Data

Classical decision trees such as C4.5 and CART partition the feature space using axis-parallel splits. Oblique decision trees use the oblique splits based on linear combinations of features to potentially simplify the boundary structure. Although oblique decision trees have higher generalization accuracy, most oblique split methods are not directly conducive to the categorical data and are computationally expensive. In this paper, we propose a multiway splits decision tree (MSDT) algorithm, which adopts feature weighting and clustering. This method can combine multiple numerical features, multiple categorical features, or multiple mixed features. Experimental results show that MSDT has excellent performance for multiple types of data.

Data-Informed Guideposts for Decision Making in Enhanced Conversational Recast Treatment

Background To maximize treatment efficiency, it would be useful to determine how long to continue a treatment approach before concluding that it is not effective for a particular client, whether and when generalization of treatment is likely to occur, and at what point to end treatment once a child is approaching mastery. Method We analyzed aggregate data from 117 preschoolers with developmental language disorder from a decade of treatment studies on Enhanced Conversational Recast therapy to determine whether the timing of treatment response impacts its overall effectiveness and whether certain levels of accuracy during treatment enable 100% accurate generalization after treatment ends. Results We found that children who take longer than 10 days to answer one item correctly during treatment are unlikely to ever respond to the treatment approach. Generalization accuracy closely followed treatment accuracy, suggesting the two are tightly linked for this treatment method. We did not find evidence that attaining a certain level of accuracy below 100% during treatment enabled children to generalize with 100% accuracy after treatment ended. Conclusions Clinicians using Enhanced Conversational Recast treatment can use these markers to help make evidence-based decisions in their practice regarding how long to continue treatment. Importantly, these data suggest that stopping treatment before a child has attained 100% accuracy (for at least three sessions) does not ensure that a child will ever reach 100% accuracy on their own.

Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation

Autonomous dexterous manipulation relies on the ability to recognize an object and detect its slippage. Dynamic tactile signals are important for object recognition and slip detection. An object can be identified based on the acquired signals generated at contact points during tactile interaction. The use of vibrotactile sensors can increase the accuracy of texture recognition and preempt the slippage of a grasped object. In this work, we present a Deep Learning (DL) based method for the simultaneous texture recognition and slip detection. The method detects non-slip and slip events, the velocity, and discriminate textures—all within 17 ms. We evaluate the method for three objects grasped using an industrial gripper with accelerometers installed on its fingertips. A comparative analysis of convolutional neural networks (CNNs), feed-forward neural networks, and long short-term memory networks confirmed that deep CNNs have a higher generalization accuracy. We also evaluated the performance of the highest accuracy method for different signal bandwidths, which showed that a bandwidth of 125 Hz is enough to classify textures with 80% accuracy.

Automated recognition of ultrasound cardiac views based on deep learning with graph constraint

ObjectiveIn Transthoracic echocardiographic (TTE) examination, it is essential to identify the cardiac views accurately. Computer-aided recognition is expected to improve the accuracy of the TTE examination.MethodsThis paper proposes a new method for automatic recognition of cardiac views based on deep learning, including three strategies. First, A spatial transform network is performed to learn cardiac shape changes during the cardiac cycle, which reduces intra-class variability. Second, a channel attention mechanism is introduced to adaptively recalibrates channel-wise feature responses. Finally, unlike conventional deep learning methods, which learned each input images individually, the structured signals are applied by a graph of similarities among images. These signals are transformed into the graph-based image embedding, which act as unsupervised regularization constraints to improve the generalization accuracy.ResultsThe proposed method was trained and tested in 171792 cardiac images from 584 subjects. Compared with the known result of the state of the art, the overall accuracy of the proposed method on cardiac image classification is 99.10% vs. 91.7%, and the mean AUC is 99.36%. Moreover, the overall accuracy is 98.15%, and the mean AUC is 98.96% on an independent test set with 34211 images from 100 subjects.ConclusionThe method of this paper achieved the results of the state of the art, which is expected to be an automated recognition tool for cardiac views recognition. The work confirms the potential of deep learning on ultrasound medicine.

A novel approach for finding crucial node using ELECTRE method

Terrorist network may be defined as collection of suspected terrorist nodes which may function in disguise towards accomplishing a terrorist activity. They use extensive communication channel for sharing crucial information. Terrorist network analysis is highly efficacious for intelligence analysis and deriving useful conclusions from available data. Computer Science and Network analysis act as pertinent fields for the study and graphical interpretation of these networks. In this paper, we examine the 26/11 Mumbai attack terrorist network dataset and employ the ELECTRE method for identification of key node in the terrorist network. ELECTRE is an effective multi-criteria decision-making model. It provides a framework for structuring a decision problem integrates the quantitative and qualitative factors of the problem and facilitates easy computation. From the 26/11 Mumbai attack dataset of terrorist network, we have determined that out of several terrorists in the network “Wassi” was the momentous and mastermind of all. The proposed work also demonstrates improvement of result in terms of concurrence, generalization accuracy and genuineness. Based on the solution of ELECTRE framework, it is resolved that the obtained (terrorist) nodes will step up the work of law enforcement agencies and enable them to confine their focus on important members of the terrorist network. Identification of key terrorist is highly important for developing long-term strategies to counter forthcoming terrorist attacks. It can be better implemented during the development of smart city especially for India.

Cost-Accuracy Aware Adaptive Labeling for Active Learning

Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for given classifier under a budget constraint. However, in many real settings, different labelers have different labeling costs and can yield different labeling accuracies. Moreover, a given labeler may exhibit different labeling accuracies for different instances. This setting can be referred to as active learning with diverse labelers with varying costs and accuracies, and it arises in many important real settings. It is therefore beneficial to understand how to effectively trade-off between labeling accuracy for different instances, labeling costs, as well as the informativeness of training instances, so as to achieve the best generalization performance at the lowest labeling cost. In this paper, we propose a new algorithm for selecting instances, labelers (and their corresponding costs and labeling accuracies), that employs generalization bound of learning with label noise to select informative instances and labelers so as to achieve higher generalization accuracy at a lower cost. Our proposed algorithm demonstrates state-of-the-art performance on five UCI and a real crowdsourcing dataset.

7 Artificial Intelligence and Machine Learning as a Predictive Tool for Acute Kidney Injury in Trauma and Severely Burned Patients

Introduction Kidney injury doubles burn mortality—thus, early prediction of acute kidney injury (AKI) in the burn population could benefit from artificial intelligence (AI) and machine learning (ML). Our objective in this study was to build and assess the theoretical performances of such AI/ML algorithms and to develop generalizable models that could augment AKI recognition. Methods Two databases containing patients that received neutrophil gelatinase associated lipocalin (NGAL), creatinine, N-terminal pro-B-type natriuretic peptide (NT-proBNP) and urine output (UOP) measurements at admission were used to train, test, and generalize the AI/ML models. Models were first optimized in Cohort A for predicting AKI in Cohort B. Cohort A (n = 50) was based on a retrospective dataset of adult (age³18 years) burn patients, while Cohort B (n = 51) consisted of prospectively enrolled adult burned or non-burned trauma patients at risk for AKI. We employed a grid search and cross validation approach in building 68,100 unique ML models from five distinct ML approaches: logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) which enabled us to find the most accurate ML models. Results The best generalization accuracy (86%), sensitivity (91%), and specificity (85%) with NGAL alone was noted with LR, SVM and RF models. Generalizability prediction accuracy, sensitivity and specificity were respectively highest with the optimized DNN model (92%, 100%, and 90%) and the k-NN model (92%, 91%, and 93%) when tested with Cohort B using all four biomarkers. k-NN provided best generalization accuracy (84%) without NGAL using only NT-proBNP and creatinine, followed by DNN using creatinine only with an accuracy of 82%. AI/ML algorithms using results obtained at admission accelerated the average (SD) time to AKI prediction by 61.8 (32.5) hours. Conclusions NGAL is analytically superior to traditional AKI biomarkers such as creatinine and UOP. With machine learning, the AKI predictive capability of NGAL can be further enhanced and accelerated when combined with NT-proBNP, UOP, and creatinine. Applicability of Research to Practice Without NGAL, machine learning models continue to provide robust means in accelerating the prediction of AKI using both common and biomarkers of cardiorenal dysfunction.

A Novel Identification Method for Apple (Malus domestica Borkh.) Cultivars Based on a Deep Convolutional Neural Network with Leaf Image Input

The innovation of germplasm resources and the continuous breeding of new varieties of apples (Malus domestica Borkh.) have yielded more than 8000 apple cultivars. The ability to identify apple cultivars with ease and accuracy can solve problems in apple breeding related to property rights protection to promote the healthy development of the global apple industry. However, the existing methods are inconsistent and time-consuming. This paper proposes an efficient and convenient method for the classification of apple cultivars using a deep convolutional neural network with leaf image input, which is the delicate symmetry of a human brain learning. The model was constructed using the TensorFlow framework and trained on a dataset of 12,435 leaf images for the identification of 14 apple cultivars. The proposed method achieved an overall accuracy of 0.9711 and could successfully avoid the over-fitting problem. Tests on an unknown independent testing set resulted in a mean accuracy, mean error, and variance of μ a c c = 0.9685 , μ ε = 0.0315 , and σ 2 = 1.89025 E − 4 , respectively, indicating that the generalization accuracy and stability of the model were very good. Finally, the classification performance for each cultivar was tested. The results show that model had an accuracy of 1.0000 for Ace, Hongrouyouxi, Jazz, and Honey Crisp cultivars, and only one leaf was incorrectly identified for 2001, Ada Red, Jonagold, and Gold Spur cultivars, with accuracies of 0.9787, 0.9800, 0.9773, and 0.9737, respectively. Jingning1 and Pinova cultivars were classified with the lowest accuracies, with 0.8780 and 0.8864, respectively. The results also show that the genetic relationship between cultivars Shoufu 3 and Yanfu 3 is very high, which is mainly because they were both selected from a red mutation of Fuji and bred in Yantai City, Shandong Province, China. Generally, this study indicates that the proposed deep learning model is a novel and improved solution for apple cultivar identification, with high generalization accuracy, stable convergence, and high specificity.