New Metric to Evaluate Cardiac Anisotropic Mechanics by Directional High-Frequency Ultrasound-Based Transverse Wave Elastography.

The evaluation of cardiac anisotropic mechanics is important in the diagnosis of heart disease. However, other representative ultrasound imaging-based metrics, which are capable of quantitatively evaluating anisotropic cardiac mechanics, are insufficient for accurately diagnosing heart disease due to the influence of viscosity and geometry of cardiac tissues. In this study, we propose a new ultrasound imaging-based metric, maximum cosine similarity (MaxCosim), for quantifying anisotropic mechanics of cardiac tissues by evaluating the periodicity of the transverse wave speeds depending on the measurement directions using ultrasound imaging. We developed a high-frequency ultrasound-based directional transverse wave imaging system to measure the transverse wave speed in multiple directions. The ultrasound imaging-based metric was validated by performing experiments on 40 rats randomly assigned to four groups; three doxorubicin (DOX) treatment groups received 10, 15, or 20 mg/kg DOX, while the control group received 0.2 mL/kg saline. In each heart sample, the developed ultrasound imaging system allowed measuring transverse wave speeds in multiple directions, and the new metric was then calculated from 3-D ultrasound transverse wave images to evaluate the degree of anisotropic mechanics of the heart sample. The results of the metric were compared with histopathological changes for validation. A decrease in the MaxCosim value was observed in the DOX treatment groups, with the degree of decrease depending on the dose. These results are consistent with the histopathological features, suggesting that our ultrasound imaging-based metric can quantify the anisotropic mechanics of cardiac tissues and potentially be used for the early diagnosis of heart disease.

Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.

BACKGROUND AND OBJECTIVE: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. METHODS: We obtained coronary artery images by echocardiography of children (n = 138 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. RESULTS: SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 81.12%, a sensitivity of 84.06%, and a specificity of 58.46%. CONCLUSIONS: The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.

Ex Vivo Evaluation of Mechanical Anisotropic Tissues with High-Frequency Ultrasound Shear Wave Elastography.

The use of imaging devices to assess directional mechanics of tissues is highly desirable. This is because the directional mechanics depend on fiber orientation, and altered directional mechanics are closely related to the pathological status of tissues. However, measuring directional mechanics in tissues with high-stiffness is challenging due to the difficulty of generating localized displacement in these tissues using acoustic radiation force, a general method for generating displacement in ultrasound-based elastography. In addition, common ultrasound probes do not provide rotational function, which makes the measurement of directional mechanics inaccurate and unreliable. Therefore, we developed a high-frequency ultrasound mechanical wave elastography system that can accommodate a wide range of tissue stiffness and is also equipped with a motorized rotation stage for precise imaging of directional mechanics. A mechanical shaker was applied to the elastography system to measure tissues with high-stiffness. Phantom and ex vivo experiments were performed. In the phantom experiments, the lateral and axial resolution of the system were determined to be 144 µm and 168 µm, respectively. In the ex vivo experiments, we used swine heart and cartilage, both of which are considered stiff. The elastography system allows us to acquire the directional mechanics with high angular resolution in the heart and cartilage. The results demonstrate that the developed elastography system is capable of imaging a wide range of tissues and has high angular resolution. Therefore, this system might be useful for the diagnostics of mechanically anisotropic tissues via ex vivo tests.

Actuator Fault Detection for Unmanned Ground Vehicles Considering Friction Coefficients.

This paper proposes an actuator fault detection method for unmanned ground vehicle (UGV) dynamics with four mecanum wheels. The actuator fault detection method is based on unknown input observers for linear parameter varying systems. The technical novelty of current work compared to similar work in the literature is that wheel frictions are directly taken into account in the dynamics of UGV, and unknown input observers are developed accordingly. Including the wheel friction, the vehicle dynamics are in the form of linear parameter varying systems. Friction estimation is also discussed in this work, and the effect of friction mismatch was quantitatively investigated by simulations. The effectiveness of proposed method was evaluated under various operation scenarios of the UGV.

Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study.

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.

EDOVE: Energy and Depth Variance-Based Opportunistic Void Avoidance Scheme for Underwater Acoustic Sensor Networks.

Underwater Acoustic Sensor Network (UASN) comes with intrinsic constraints because it is deployed in the aquatic environment and uses the acoustic signals to communicate. The examples of those constraints are long propagation delay, very limited bandwidth, high energy cost for transmission, very high signal attenuation, costly deployment and battery replacement, and so forth. Therefore, the routing schemes for UASN must take into account those characteristics to achieve energy fairness, avoid energy holes, and improve the network lifetime. The depth based forwarding schemes in literature use node's depth information to forward data towards the sink. They minimize the data packet duplication by employing the holding time strategy. However, to avoid void holes in the network, they use two hop node proximity information. In this paper, we propose the Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) scheme to gain energy balancing and void avoidance in the network. EDOVE considers not only the depth parameter, but also the normalized residual energy of the one-hop nodes and the normalized depth variance of the second hop neighbors. Hence, it avoids the void regions as well as balances the network energy and increases the network lifetime. The simulation results show that the EDOVE gains more than 15 % packet delivery ratio, propagates 50 % less copies of data packet, consumes less energy, and has more lifetime than the state of the art forwarding schemes.