OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis

Ultrasound (US) imaging is indispensable in clinical practice. To diagnose certain diseases, sonographers must observe corresponding dynamic anatomic structures to gather comprehensive information. However, the limited availability of specific US video cases causes teaching difficulties in identifying corresponding diseases, which potentially impacts the detection rate of such cases. The synthesis of US videos may represent a promising solution to this issue. Nevertheless, it is challenging to accurately animate the intricate motion of dynamic anatomic structures while preserving image fidelity. To address this, we present a novel online feature-decoupling framework called OnUVS for high-fidelity US video synthesis. Our highlights can be summarized by four aspects. First, we introduced anatomic information into keypoint learning through a weakly-supervised training strategy, resulting in improved preservation of anatomical integrity and motion while minimizing the labeling burden. Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator. Third, we adopted a multiple-feature discriminator to extract a comprehensive range of visual cues, thereby enhancing the sharpness and fine details of the generated videos. Fourth, we constrained the motion trajectories of keypoints during online learning to enhance the fluidity of generated videos. Our validation and user studies on in-house echocardiographic and pelvic floor US videos showed that OnUVS synthesizes US videos with high fidelity.Comment: 14 pages, 13 figures and 6 table

Ultrasound-Augmented Laparoscopy

Laparoscopic surgery is perhaps the most common minimally invasive procedure for many diseases in the abdomen. Since the laparoscopic camera provides only the surface view of the internal organs, in many procedures, surgeons use laparoscopic ultrasound (LUS) to visualize deep-seated surgical targets. Conventionally, the 2D LUS image is visualized in a display spatially separate from that displays the laparoscopic video. Therefore, reasoning about the geometry of hidden targets requires mentally solving the spatial alignment, and resolving the modality differences, which is cognitively very challenging. Moreover, the mental representation of hidden targets in space acquired through such cognitive medication may be error prone, and cause incorrect actions to be performed. To remedy this, advanced visualization strategies are required where the US information is visualized in the context of the laparoscopic video. To this end, efficient computational methods are required to accurately align the US image coordinate system with that centred in the camera, and to render the registered image information in the context of the camera such that surgeons perceive the geometry of hidden targets accurately. In this thesis, such a visualization pipeline is described. A novel method to register US images with a camera centric coordinate system is detailed with an experimental investigation into its accuracy bounds. An improved method to blend US information with the surface view is also presented with an experimental investigation into the accuracy of perception of the target locations in space

The Reliability, Practicality and Acceptability of Using Ultrasonography to Monitor the Progress of Labour and Delivery

Introduction: It had been suggested by a number of recent studies that ultrasonography could become an alternative to digital vaginal examination (VE) for assessing the progress of pregnant women in labour. However, no systematic review and meta-analysis on the effectiveness of ultrasonography was available. Systematic Review: A systematic review and meta-analysis was conducted to investigate the success rate of ultrasonography in comparison with digital VE and the level of agreement between the two methods, in terms of estimating fetal head position, head station and cervical dilatation. Systematic Review Findings: This review found that ultrasonography has a higher success rate than digital VE in estimating fetal head position. Ultrasonography was also in high agreement with digital VE in estimating cervical dilatation, with insignificant difference in the success rate of the two methods in terms of detecting cervical dilatation. There was also a significant correlation between the two methods in estimating head station. However, it was also found by the review that, existing primary studies were mainly conducted in tertiary settings of developed countries. Further research was therefore needed from the perspective of non-tertiary settings and also from developing country settings. In addition, further research was also needed to assess the diagnostic performance of ultrasound in detecting active labour, since it is associated with cervical dilatation. The diagnostic performance of ultrasound in detecting engaged fetal head had also not been investigated, which is necessary because it is associated with head station. Primary Research Aim: As a consequence of these systematic review findings, a primary study was conducted in another clinical setting in a developing country. The aim was to investigate the reproducibility, practicality and acceptability of using ultrasonography to monitor the progress of pregnant women in labour. Research Methods: A cross-sectional study was conducted in a teaching hospital in Ghana. The agreement between ultrasound and digital VE was statistically analysed for the estimation of fetal head position, head station and cervical dilatation. Further statistical analysis was conducted on the diagnostic performance of ultrasound in detecting engaged fetal head, and the diagnostic performance of ultrasound in detecting active labour. A quantitative survey of mothers’ acceptance of intrapartum ultrasound was also conducted. Lastly, caregivers’ views on the practicality of using ultrasound in this developing country setting was also investigated in a qualitative survey. Results of Primary Research: The results regarding reproducibility were as follows: (i) a high between-method agreement was found in the estimation of cervical dilatation, with high ultrasound sensitivity and specificity in detecting active labour; (ii) a statistically significant between-method agreement was found in the estimation of head station, with high ultrasound sensitivity and specificity in detecting engaged fetal head; (iii) a weak between-method agreement was found in the estimation of fetal head position, with ultrasound having a higher success rate than digital VE. The results regarding acceptability showed that most mothers accepted the use of intrapartum ultrasound, and were willing to have the procedure for their future care during labour and childbirth. They also preferred ultrasound to digital VE. With regards to practicality, the responses of caregivers indicate that the introduction of intrapartum ultrasound in this setting could serve as a good complement to digital VE in a number of ways. However, putting it into practice would require wider availability of physical and technical resources. Conclusion: The findings of the reproducibility study were consistent with existing studies in other clinical settings which were investigated in the systematic review. This suggests that ultrasound is a reliable method for assessing the progress of pregnant women in labour. In addition, the unique contribution to existing knowledge obtained from this study was a high ultrasound sensitivity and specificity in detecting active labour and engaged fetal head which were reported for the first time. The findings on mothers’ acceptability were also consistent with existing studies in other settings, which is an indication that there is high acceptance of intrapartum ultrasound by mothers from different settings and cultures. Lastly, caregivers’ views on the practicality of the use of ultrasound during labour indicate that the regular use of intrapartum ultrasound for assessing the progress of labour in pregnant women may require additional resources to make it practicable in this and other similar settings

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Evaluation of an indwelling bolus equipped with a triaxial accelerometer for the characterisation of the diurnal pattern of reticuloruminal contractions

Introduction: pages 8-

Machine-Learning-Powered Cyber-Physical Systems

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography