A comprehensive multimodality heart motion prediction algorithm for robotic‐assisted beating heart surgery

Mansouri, Saeed; Farahmand, Farzam; Vossoughi, Gholamreza; Ghavidel, Alireza Alizadeh

doi:10.1002/rcs.1975

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…General health status and activity prediction were proposed in [105,216]. An interesting study related to predicting heart motion to assist in robotic heart surgery was detailed in [217].…”

Section: Service-based Monitoring Systemsmentioning

confidence: 99%

“…On the other hand, advancements in robotics introduced new opportunities for cardiac healthcare, especially with the great challenge of limited medical resources. Some researchers investigated the dimensions of the practical use of robotic-assisted heart surgeries [217,266]. Others studied the possibility of having a robot assistant providing medical feedback, diagnosis, and notifications [267,268].…”

Section: Ecg Futuristic Monitoring Systemsmentioning

confidence: 99%

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ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

231

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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“…General health status and activity prediction were proposed in [105,216]. An interesting study related to predicting heart motion to assist in robotic heart surgery was detailed in [217].…”

Section: Service-based Monitoring Systemsmentioning

confidence: 99%

Section: Ecg Futuristic Monitoring Systemsmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

231

View full text Add to dashboard Cite

show abstract

“…Home robots that support elderly people and help in coping with the problems of aging, such as disorders affecting memory, motor functions, and muscle weakness, can overcome the shortage of caregivers and healthcare providers [45,53][14]. Robotic-assisted minimally invasive surgeries such as heart surgery [46], neurosurgery [44], liver surgery [42], and ultrasound [28] and MRI-guided [39] positioning and alignment, and reduced running time. Surgical maps are preoperatively generated by AI-based systems to achieve optimum precision [48]; thus, before initiating a procedure, the surgeon can review and modify the surgical plan and conduct a mental walk-through of the procedure.…”

Section: A Artificial Intelligence For Schmentioning

confidence: 99%

“…Home robots that support elderly people and help in coping with the problems of aging, such as disorders affecting memory, motor functions, and muscle weakness, can overcome the shortage of caregivers and healthcare providers [14] , [45] , [53] . Robotic-assisted minimally invasive surgeries such as heart surgery [46] , neurosurgery [44] , liver surgery [42] , and ultrasound [28] and MRI-guided [39] robotic interventions are more established now. In addition to improving surgical workflow, robotic-assisted surgical interventions have facilitated precise robotic bone machining, enhanced bone implantation, optimization of implant positioning and alignment, and reduced running time.…”

Section: Smart and Connected Health: Review Classification And Comparisonmentioning

confidence: 99%

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

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Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

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