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Illustration of bit per pixel density at 500 kbps and 360 × 240 pixels with (a) ROI compression: ROI bpp = 0.42; ROI bit rate = 0.42 × 360 × 240 × 0.25 × 30 = 270 kbps; BKGRND bit rate = 500 − 270 = 230 kbps; BKGRND bpp = 230000/(30 × 360 × 240 × 0.75) = 0.117 and (b) uniform compression: overall bpp = 500000/(30 × 360 × 240) = 0.193.
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In real-time remote diagnosis of emergency medical events, mobility can be enabled by wireless video communications. However, clinical use of this potential advance will depend on definitive and compelling demonstrations of the reliability of diagnostic quality video. Because the medical domain has its own fidelity criteria, it is important to inco...
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Citations
... research delves into diverse topics, including Medical Video Modality-Aware (m-aware) systems, which adapt encoding, transmission, and evaluation based on individual video modality properties [5][6][7][8][9]. Multilayer and cross-layer optimization systems aim for optimal performance [10][11][12][13], alongside studies focusing on clinical quality assessment protocols and recommendations [14][15][16][17][18][19][20][21][22][23]. ...
... Nevertheless, the clustering algorithm demonstrates its ability to recover a specific segment of the DRN infrastructure with varying delay values in response to the load settings. These delay values, however, may be considered acceptable given the requirements of the envisioned DRN services [5,8]. ...
This paper investigates a multifaceted approach to fortifying telemedicine services within a self-powered Disaster Recovery Network (DRN) infrastructure. It introduces an array of innovative methodologies and algorithms tailored to address the logistical complexities of constructing an environmentally friendly DRN infrastructure. Additionally, it delves into the fundamental factors influencing the system's behavior, defines key performance indicators, and outlines performance measurement techniques. The study emphasizes the critical need for seamless integration of diverse reliability methods by introducing a novel blockchain-based DRN clustering algorithm, coupled with an intelligently managed solar energy system. Specifically, it presents the "Duty Cycle Estimation (DCE) – Event Driven Duty Cycling (EDDC)" technique using the Ubicom IP 2022 platform. Moreover, it proposes an experimental platform for comprehensive evaluation, assessing network performance, practicality, power efficiency, and resilience under various failure scenarios. This comprehensive assessment serves to advance the field and pave the way for robust and reliable telemedicine services in the face of disaster.
... Examples of digital health solutions used in sensitive settings include therapeutic devices administered by clinicians [76], therapeutic tools administered in home settings [77][78][79], monitoring systems in hospital settings [80][81], dual-purpose interventions which explicitly collect patient health information to train machine learning models [82][83][84], pediatric healthcare interventions disguised to the child as a game [85][86], and wearable devices [87]. Many of these therapeutic and diagnostic devices collect potentially sensitive audio, image, and video streams for clinical use [88][89][90][91], and these data streams are often shared with clinicians or even crowdsourced with the consent of the patient. Furthermore, several digital therapies are used in home settings, and such rich data streams are filled with protected health information accompanied by potentially sensitive identifiable information such as the patient's face, images and video of the patient's home, and audio recordings of the patient or their family while using the device. ...
... [18][19][20][21] Measuring pROM and the business value of IT in healthcare will provide guidance on the efficiency and efficacy of these virtual care methods. 7,[21][22][23][24] The setup/resource requirements. adequate setup is essential to maintain high-quality virtual healthcare delivery. ...
Virtual encounters have experienced an exponential rise amid the current COVID-19 crisis. This abrupt change, seen in response to unprecedented medical and environmental challenges, has been forced upon the orthopaedic community. However, such changes to adopting virtual care and technology were already in the evolution forecast, albeit in an unpredictable timetable impeded by regulatory and financial barriers. This adoption is not meant to replace, but rather augment established, traditional models of care while ensuring patient/provider safety, especially during the pandemic. While our department, like those of other institutions, has performed virtual care for several years, it represented a small fraction of daily care. The pandemic required an accelerated and comprehensive approach to the new reality. Contemporary literature has already shown equivalent safety and patient satisfaction, as well as superior efficiency and reduced expenses with musculoskeletal virtual care (MSKVC) versus traditional models. Nevertheless, current literature detailing operational models of MSKVC is scarce. The current review describes our pre-pandemic MSKVC model and the shift to a MSKVC pandemic workflow that enumerates the conceptual workflow organization (patient triage, from timely care provision based on symptom acuity/severity to a continuum that includes future follow-up). Furthermore, specific setup requirements (both resource/personnel requirements such as hardware, software, and network connectivity requirements, and patient/provider characteristics respectively), and professional expectations are outlined. MSKVC has already become a pivotal element of musculoskeletal care, due to COVID-19, and these changes are confidently here to stay. Readiness to adapt and evolve will be required of individual musculoskeletal clinical teams as well as organizations, as established paradigms evolve.
Cite this article: Bone Joint Open 2020;1-6:272–280.
... [18][19][20][21] Measuring pROM and the business value of IT in healthcare will provide guidance on the efficiency and efficacy of these virtual care methods. 7,[21][22][23][24] The setup/resource requirements. adequate setup is essential to maintain high-quality virtual healthcare delivery. ...
Virtual encounters have experienced an exponential rise amid the current COVID-19 crisis. This abrupt change, seen in response to unprecedented medical and environmental challenges, has been forced upon the orthopaedic community. However, such changes to adopting virtual care and technology were already in the evolution forecast, albeit in an unpredictable timetable impeded by regulatory and financial barriers. This adoption is not meant to replace, but rather augment established, traditional models of care while ensuring patient/provider safety, especially during the pandemic. While our department, like those of other institutions, has performed virtual care for several years, it represented a small fraction of daily care. The pandemic required an accelerated and comprehensive approach to the new reality. Contemporary literature has already shown equivalent safety and patient satisfaction, as well as superior efficiency and reduced expenses with musculoskeletal virtual care (MSKVC) versus traditional models. Nevertheless, current literature detailing operational models of MSKVC is scarce. The current review describes our pre-pandemic MSKVC model and the shift to a MSKVC pandemic workflow that enumerates the conceptual workflow organization (patient triage, from timely care provision based on symptom acuity/severity to a continuum that includes future follow-up). Furthermore, specific setup requirements (both resource/personnel requirements such as hardware, software, and network connectivity requirements, and patient/provider characteristics respectively), and professional expectations are outlined. MSKVC has already become a pivotal element of musculoskeletal care, due to COVID-19, and these changes are confidently here to stay. Readiness to adapt and evolve will be required of individual musculoskeletal clinical teams as well as organizations, as established paradigms evolve.
Cite this article: Bone Joint Open 2020;1-6:272–280.
... Despite these advancements, mHealth video communication systems have not experienced wider clinical usage in spite of successes of proof-of-concept and pilot studies [121,8,18,135,19,136,115,119,137,120,81,138]. This is partly due to the fact that most studies are modalityspecific, rely on specific video compression and wireless technologies, do not address computational complexity, and more importantly, do not facilitate efficient adaptation to real-time video communication constraints. ...
... Related research focuses on switching between pre-encoded video content in response to available bandwidth [18,135,19,115,113,138]. These approaches cannot guarantee real-time encoding, neither minimum clinical video quality levels can be secured against bandwidth fluctuations for which a pre-encoded instance has not been foreseen. ...
... In [18], for pediatric respiratory distress videos, the authors consider switching between six precomputed states in response to changes of available bandwidth. To both enhance video's diagnostic capacity and preserve network resources, a diagnostically relevant encoding approach was employed, where the region of diagnostic interest (d-ROI) was encoded in higher quality than the background. ...
The significant number of mobile health (mHealth) systems developed over the past decade reflect the broad applicability spectrum of such systems and services in standard clinical practice. In terms of medical video communications, application scenarios range, but are not limited, from remote diagnosis and care, to emergency response, second opinion provision, and home monitoring. Integration of medical video communication systems in the healthcare provision pathway can significantly enhance the quality of care, while reducing hospitalization times and associated healthcare costs.
The challenge lies in delivering sufficiently high video resolutions and frame rates with the low-delay and low packet loss rates requirements that will accommodate an equivalent level of clinical experience to that of in-hospital examinations. The latter requires real-time control of the video streaming process so as to facilitate the adequate levels of clinical video quality required to support reliable diagnosis. The objective of this PhD thesis is to provide a scalable, video modality, encoder, and wireless network agnostic framework, that will support real-time adaptation to time-varying wireless networks’ state while guaranteeing diagnostically lossless video communications and conforming to end-user device constraints for real-time performance.
More specifically, the approach is based on multi-objective optimization, that jointly maximizes the encoded video’s quality and encoding rate, while minimizing bitrate demands. For this purpose, a dense encoding space is constructed, and logarithmic linear regression is used to estimate forward prediction models for quality, bitrate, and computational complexity. The prediction models are then used by the proposed adaptive control framework that can fine-tune video encoding based on real-time constraints. The proposed framework is validated using a leave-one-out algorithm applied to ten ultrasound videos of the common carotid artery. The prediction models can estimate Structural SIMilarity (SSIM) 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 (GOP) level is demonstrated using the High Efficiency Video Coding (HEVC) standard. The effectiveness of the proposed framework compared to static, non-adaptive 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.
... Despite these advancements, mHealth video communication systems have not experienced wider clinical usage in spite of successes of proof-of-concept and pilot studies [7]- [20]. This is partly due to the fact that most studies are modality-specific, rely on specific video compression and wireless technologies, do not address computational complexity, and more importantly, do not facilitate efficient adaptation to real-time video communication constraints. ...
... Related research focuses on switching between pre-encoded video content in response to available bandwidth [7]- [9], [12], [13], [19]- [20]. On the one hand, these approaches cannot guarantee real-time encoding. ...
... In [7], for pediatric respiratory distress videos, the authors consider switching between six precomputed states in response to changes of available bandwidth. To enhance video's diagnostic capacity and preserve network resources, a diagnostically relevant encoding approach was employed, where the diagnostic region of interest (d-ROI) was encoded in higher quality than the background. ...
The wider adoption of mobile Health (mHealth) 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 (SSIM) 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 (GOP) level is demonstrated using the High Efficiency Video Coding (HEVC) standard. The effectiveness of the proposed framework compared to static, non-adaptive 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.
... Diagnostic Losslessness (as opposed to Mathematical Losslessness, implying no loss of any digital information) is required for the transmission of telemedicine video, i.e., lossy compression may exist, as long as it does not compromise visual medical assessment in any way and hence suffices for making a diagnostic evaluation [8] . Telemedicine data, on the other hand, needs to be transmitted with Mathematical Losslessness, therefore it cannot be transmitted as BK, nor as BE traffic. ...
... For this reason, the requirements for transmitting diagnosis-grade video [29] are different from those of video conferencing (e.g., in terms of color faithfulness). The work in [8] studied the relation between the network bandwidth dedicated to the video transfer and the diagnosis quality, and compared uniform and Region-of-Interest (ROI) video encoding. In the case of ROI, a subset of the picture (the region of interest) is encoded at higher quality compared to the video background. ...
... In the case of ROI, a subset of the picture (the region of interest) is encoded at higher quality compared to the video background. ROI encoding is shown in [8] to save 53% of the bandwidth (the required mean bit rate of telemedicine video dropped from 1.077 Mbps to 500 Kbps). Hence, we use the video traffic model from [25] in order to simulate the traffic of a H.264 trace with mean equal to 500 Kbps and standard deviation equal to 600 Kbps, assuming that at first the region of interest might be unclear but soon it will be defined by the remote physician. ...
The major disadvantage of the Enhanced Distributed Channel Access (EDCA, the contention-based channel access function of 802.11e) is that it is unable to guarantee priority access to higher priority traffic in the presence of significant traffic loads from low priority users. This problem is enhanced by the continuously growing number of multimedia applications and the popularity of Wireless Local Area Networks (WLANs). Hence, solutions in scheduling multimedia traffic transmissions need to take into account both the Quality of Service (QoS) requirements and the Quality of Experience (QoE) associated with each application, especially those of urgent traffic, like telemedicine, which carries critical information regarding the patients’ condition. In this work, we propose an easy-to-implement token-based and self policing-based scheduling scheme combined with a mechanism designed to mitigate congestion. Our approach is shown to guarantee priority access to telemedicine traffic, to satisfy its QoS requirements (delay, packet dropping) and to offer high telemedicine video QoE while preventing bursty video nodes from over-using the medium.
... However, what one finds in the some existing literature on wireless systems for telemedicine [19] is a discussion of wireless technologies but limited simulation or testing for specific wireless systems. For example, in [3] though a WiMAX simulation was performed, the authors did not state what channel model was used and employed a non-standard wavelet codec, whereas in [9], no specific wireless technology was simulated and an unrealistic error pattern was tested. ...
This paper presents the exploratory stages of a project to develop Telemedicine services in the Republic of Kazakhstan. The project intends to take advantage of recent technological developments. It proposes a new video-encoding scheme as a replacement for other video codecs currently used in telemedicine systems. H.265 is suggested for telemedicine systems because it can support high-quality video streaming for telemedicine systems and at the same time it requires less bandwidth than its predecessor H.264. This perfectly fits within 4G wireless network capabilities such as WiMAX and Long Term Evolution (LTE) networks. The project targets patients whom cannot easily access the hospital to be monitored or be treated by a specialist even though this is needed for patient cases such as rehabilitation, and paraplegics. Using LTE and WiMAX to form the telemedicine framework means that the system could be used either from the home to monitor the patient or it could be used from mobile devices such as smart-phones, PDAs, or laptops. In addition to the initial results, future investigations are proposed for optimal streaming of H.265 over 4G wireless networks.
... Some researchers have focused on user defined ROIs [16], [17] while others employed automated methods [14], [18]. Rao et al. [16] proposed an elastic ROI coding algorithm for mobile medical assessments of acute childhood respiratory distress. ...
... Some researchers have focused on user defined ROIs [16], [17] while others employed automated methods [14], [18]. Rao et al. [16] proposed an elastic ROI coding algorithm for mobile medical assessments of acute childhood respiratory distress. Physicians could specify an ROI which varied from 25% to 50% of the total frame. ...
... This procedure is a major component of the clinical evaluation procedure. Acceptable video resolutions that do not compromise the geometry characteristics, frame rates that preserve clinical motion, and compression ratios (both in terms of measured PSNR and quantization parameters) that maintain diagnostically acceptable quality levels are shown for different medical video modalities in [211], [214], [215], [218], [223], [224]. ...
In this chapter we discuss the use of despeckle filtering prior to video coding for mobile-health (mHealth) medical video communications. Linked with the new High Efficiency Video Coding (HEVC) standard, the use of despeckle filtering facilitates significant bitrate gains for equivalent clinical quality, which can provide for beyond high-definition (HD) real-time communication of ultrasound video over emerging wireless networks.
