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Emerging Technologies for Healthcare during the COVID-19 Pandemic
Published in Sam Goundar, Archana Purwar, Ajmer Singh, Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, 2023
Wireless and satellite communication channels, machine learning, imaging technology, artificial intelligence, and tele-surgical robotics can be used to carry out remote surgery in different regions by supervising the surgical robots (Zeadally & Bello, 2021). Remote-controlled robotic surgery can decrease the risks and increase precision so that consequences of surgical decisions can be assessed with patient risk factors (Matheny et al., 2019). Heart failure before symptoms even arise can be found by machine learning and neural networks (Farrugia & Plutowski, 2020). Personalizing chemotherapy dosing and mapping patient response, determining the best surveillance intervals for colonoscopy exams, and best treatment options for complex diseases are types of personalized management and treatment; a patient-centered medical homes model (Matheny et al., 2019).
Internet of Medical Things: Current and Future Trends
Published in Manuel Cardona, Vijender Kumar Solanki, Cecilia E. García Cena, Internet of Medical Things, 2021
Lucia Alonso Virgos, Miguel A. Sanchez Vidales, Fernando López Hernández, J. Javier Rainer Granados
This type of robots assists surgeons in performing complex interventions with very positive results (e.g., [3]). These interventions might be related to accuracy or strength; for example, surgery in the head or the cerebral area where precision and accuracy play a crucial role. On the other hand, an example of strength-related intervention is the case of cutting a bone in such a way that healthy areas do not get damaged, such as tissues or the rest of the bone. Robotic surgery helps to obtain higher accuracy in the process, decrease time of operation, decrease time of wound healing and therefore recovery time. The robot can be guided by cameras and with the appropriate information about the patient, it can fulfill different functions; for example, this technology might change according to the need, ranging from a conventional mechanical arm up to elements of measurement. Also, advantages of remote surgery should be underscored, as it allows patients to receive a better treatment when any specialist from across the globe can assist in surgery or in offering advice.
Machine Learning Applications In Medical Image Processing
Published in Sanjay Saxena, Sudip Paul, High-Performance Medical Image Processing, 2022
Tanmay Nath, Martin A. Lindquist
Surgical robots (also known as robot-assisted surgery or robotic surgery) allow doctors to perform surgery with more precision, control, and flexibility than using conventional techniques. Currently, the ™da Vinci surgical system is the state-of-the-art technique for robotic surgery. It includes specialized “arms” for holding the instruments, camera, magnified screen, computer console, and other surgical accessories. To operate using the robotic system, the doctor makes a tiny incision in the body and inserts the surgical instruments and camera. The doctor uses the external computer console to guide the instrument to the surgical site and perform surgery. The doctor is always in control of the robot and the system responds to directions provided by the doctor.
VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
João Cartucho, Samyakh Tukra, Yunpeng Li, Daniel S. Elson, Stamatia Giannarou
Robotic surgery has revolutionised everyday surgical practice, and it has become a vital tool for many surgical procedures, enhancing surgical skills and allowing faster recovery for patients (Koh et al. 2018). Surgical robotic technologies constantly evolve with the ultimate goal to enable autonomous surgical task execution (Zhang et al. 2017a, 2017b; Zhan et al. 2020). For this purpose, a robot needs to be able to perceive the surgical scene in real-time and to precisely move surgical tools in reference to the deforming soft tissue. The standard approach for robotic scene perception is through computer vision algorithms, analysing in real-time image data captured by an endoscopic camera. However, the development and validation of surgical vision algorithms, both for traditional computer vision and for deep learning-based techniques, relies on having access to large datasets of endoscopic images and videos. Obtaining these datasets can be particularly challenging as it requires accessing expensive hardware, securing ethical approval, procuring patient consent and having regular access to hospitals. These obstacles result in a complex and time-consuming process, which contributes to delays for research and innovation in this field.
Modeling visuospatial reasoning
Published in Spatial Cognition & Computation, 2019
Robotic surgery links virtual actions to physical objects by means of a console that controls four interactive robotic arms. The da Vinci system was designed to replace conventional laparoscopy that requires the surgeon to operate while standing, using hand-held, long-shafted instruments (Bric, Lumbard, Frelich & Gould, 2016). Research on virtual laboratory experiments has shown that they are as effective as physical experiments in teaching students the principles of experimental design (Klahr, Triona & Williams, 2007). Point-and-click was as effective as grab-and-heft for several different design tasks (Triona & Klahr, 2003). The actions for other tasks, such as conventional laparoscopic versus robotic surgery, are quite different and require specialized training for physical and for virtual actions (Sridhar, Briggs, Kelly & Nathan, 2017). Virtual actions combined with auditory feedback have proven effective in teaching the blind to learn the spatial layout of a virtual building. The resulting mental image supported various navigation routes throughout the physical building (Connors, Chrastil, Sanchez & Merabet, 2014).
Secure Share: Optimal Blokchain Integration in IoT Systems
Published in Journal of Computer Information Systems, 2023
Smita Dange, Prashant Nitnaware
Healthcare systems enabled digitization to provide patients with quick, easy, and traceable service. Robotic surgery eliminates pain and shortens the patient’s hospital stay. An IoT-enabled healthcare system pushes automation. Encourages remote patient monitoring and improves response times in an emergency. In recent pandemic scenarios, IoT-enabled remote monitoring protected all healthcare system stakeholders and improved patient handling in such chaotic situations. The use of blockchain in IoT-enabled healthcare systems is an attempt to improve service quality. The rationale and appropriateness for the same is mentioned in Table 7.