AI in Medical Imaging Informatics: Current Challenges and Future Directions.

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

Risk factors for breast cancer brain metastases: a systematic review.

BACKGROUND: Brain metastasis (BM) is an increasingly common and devastating complication of breast cancer (BC). METHODS: A systematic literature search of EMBASE and MEDLINE was conducted to elucidate the current state of knowledge on known and novel prognostic factors associated with 1) the risk for BCBM and 2) the time to brain metastases (TTBM). RESULTS: A total of 96 studies involving institutional records from 28 countries were identified. Of these, 69 studies reported risk factors of BCBM, 46 factors associated with the TTBM and twenty studies examined variables for both outcomes. Young age, estrogen receptor negativity (ER-), overexpression of human epidermal factor (HER2+), and higher presenting stage, histological grade, tumor size, Ki67 labeling index and nodal involvement were consistently found to be independent risk factors of BCBM. Of these, triple-negative BC (TNBC) subtype, ER-, higher presenting histological grade, tumor size, and nodal involvement were also reported to associate with shorter TTBM. In contrast, young age, hormone receptor negative (HR-) status, higher presenting stage, nodal involvement and development of liver metastasis were the most important risk factors for BM in HER2-positive patients. CONCLUSIONS: The study provides a comprehensive and individual evaluation of the risk factors that could support the design of screening tools and interventional trials for early detection of BCBM.

The Upcoming Role for Nursing and Assistive Robotics: Opportunities and Challenges Ahead.

As an integral part of patient care, nursing is required to constantly adapt to changes in the healthcare system, as well as the wider financial and societal environment. Among the key factors driving these changes is the aging of population. Combined with an existing shortage of nursing and caregiving professionals, accommodating for the patients and elderly needs within hospitals, elderly-care facilities and at a home setting, becomes a societal challenge. Amongst the technological solutions that have evolved in response to these developments, nursing and assistive robotics claim a pivotal role. The objective of the present study is to provide an overview of today's landscape in nursing and assistive robotics, highlighting the benefits associated with adopting such solutions in standard clinical practice. At the same time, to identify existing challenges and limitations that essentially outline the area's future directions. Beyond technological innovation, the manuscript also investigates the end-users' angle, being a crucial parameter in the success of robotics solutions operating within a healthcare environment. In this direction, the results of a survey designed to capture the nursing professionals' perspective toward more informed robotics design and development are presented.

Radiogenomics for Precision Medicine With a Big Data Analytics Perspective.

Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.

Real-Time Adaptation to Time-Varying Constraints for Medical Video Communications.

The wider adoption of mobile Health video communication systems in standard clinical practice requires real-time control to provide for adequate levels of clinical video quality to support reliable diagnosis. The latter can only be achieved with real-time adaptation to time-varying wireless networks' state to guarantee clinically acceptable performance throughout the streaming session, while conforming to device capabilities for supporting real-time encoding. We propose an adaptive video encoding framework based on multi-objective optimization that jointly maximizes the encoded video's quality and encoding rate (in frames per second) while minimizing bitrate demands. For this purpose, we construct a dense encoding space and use linear regression to estimate forward prediction models for quality, bitrate, and computational complexity. The prediction models are then used in an adaptive control framework that can fine-tune video encoding based on real-time constraints. We validate the system using a leave-one-out algorithm applied to ten ultrasound videos of the common carotid artery. The prediction models can estimate structural similarity quality with a median accuracy error of less than 1%, bitrate demands with deviation error of 10% or less, and encoding frame rate within a 6% margin. Real-time adaptation at a group of pictures level is demonstrated using the high efficiency video coding standard. The effectiveness of the proposed framework compared to static, nonadaptive approaches is demonstrated for different modes of operation, achieving significant quality gains, bitrate demands reductions, and performance improvements, in real-life scenarios imposing time-varying constraints. Our approach is generic and should be applicable to other medical video modalities with different applications.

Cardiac ultrasonography over 4G wireless networks using a tele-operated robot.

This Letter proposes an end-to-end mobile tele-echography platform using a portable robot for remote cardiac ultrasonography. Performance evaluation investigates the capacity of long-term evolution (LTE) wireless networks to facilitate responsive robot tele-manipulation and real-time ultrasound video streaming that qualifies for clinical practice. Within this context, a thorough video coding standards comparison for cardiac ultrasound applications is performed, using a data set of ten ultrasound videos. Both objective and subjective (clinical) video quality assessment demonstrate that H.264/AVC and high efficiency video coding standards can achieve diagnostically-lossless video quality at bitrates well within the LTE supported data rates. Most importantly, reduced latencies experienced throughout the live tele-echography sessions allow the medical expert to remotely operate the robot in a responsive manner, using the wirelessly communicated cardiac ultrasound video to reach a diagnosis. Based on preliminary results documented in this Letter, the proposed robotised tele-echography platform can provide for reliable, remote diagnosis, achieving comparable quality of experience levels with in-hospital ultrasound examinations.

Medical telerobotic systems: current status and future trends.

Teleoperated medical robotic systems allow procedures such as surgeries, treatments, and diagnoses to be conducted across short or long distances while utilizing wired and/or wireless communication networks. This study presents a systematic review of the relevant literature between the years 2004 and 2015, focusing on medical teleoperated robotic systems which have witnessed tremendous growth over the examined period. A thorough insight of telerobotics systems discussing design concepts, enabling technologies (namely robotic manipulation, telecommunications, and vision systems), and potential applications in clinical practice is provided, while existing limitations and future trends are also highlighted. A representative paradigm of the short-distance case is the da Vinci Surgical System which is described in order to highlight relevant issues. The long-distance telerobotics concept is exemplified through a case study on diagnostic ultrasound scanning. Moreover, the present review provides a classification into short- and long-distance telerobotic systems, depending on the distance from which they are operated. Telerobotic systems are further categorized with respect to their application field. For the reviewed systems are also examined their engineering characteristics and the employed robotics technology. The current status of the field, its significance, the potential, as well as the challenges that lie ahead are thoroughly discussed.

An effective ultrasound video communication system using despeckle filtering and HEVC.

The recent emergence of the high-efficiency video coding (HEVC) standard promises to deliver significant bitrate savings over current and prior video compression standards, while also supporting higher resolutions that can meet the clinical acquisition spatiotemporal settings. The effective application of HEVC to medical ultrasound necessitates a careful evaluation of strict clinical criteria that guarantee that clinical quality will not be sacrificed in the compression process. Furthermore, the potential use of despeckle filtering prior to compression provides for the possibility of significant additional bitrate savings that have not been previously considered. This paper provides a thorough comparison of the use of MPEG-2, H.263, MPEG-4, H.264/AVC, and HEVC for compressing atherosclerotic plaque ultrasound videos. For the comparisons, we use both subjective and objective criteria based on plaque structure and motion. For comparable clinical video quality, experimental evaluation on ten videos demonstrates that HEVC reduces bitrate requirements by as much as 33.2% compared to H.264/AVC and up to 71% compared to MPEG-2. The use of despeckle filtering prior to compression is also investigated as a method that can reduce bitrate requirements through the removal of higher frequency components without sacrificing clinical quality. Based on the use of three despeckle filtering methods with both H.264/AVC and HEVC, we find that prior filtering can yield additional significant bitrate savings. The best performing despeckle filter (DsFlsmv) achieves bitrate savings of 43.6% and 39.2% compared to standard nonfiltered HEVC and H.264/AVC encoding, respectively.