The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.
Cardiovascular diseases remain as the most common cause of death worldwide. Remotely manipulated robotic systems are utilized to perform minimally invasive endovascular interventions. The main benefits of this methodology include reduced recovery time, improvement of clinical skills and procedural facilitation. Currently, robotic assistance, precision, and stability of instrument manipulation are compensated by the lack of haptic feedback and an excessive amount of radiation to the patient. This paper proposes a novel master-slave robotic platform that aims to bring the haptic feedback benefit on the master side, providing an intuitive user interface, and clinical familiar workflow. The slave robot is capable of manipulating conventional catheters and guidewires in multi-modal imaging environments. The system has been initially tested in a phantom cannulation study under fluoroscopic guidance, evaluating its reliability and procedural protocol. As the slave robot has been entirely produced by additive manufacturing and using pneumatic actuation, MR compatibility is enabled and was evaluated in a preliminary study. Results of both studies strongly support the applicability of the robot in different imaging environments and prospective clinical translation.
Cardiovascular diseases are the most common cause of global death. Endovascular interventions, in combination with advanced imaging technologies, are promising approaches for minimally invasive diagnosis and therapy. More recently, teleoperated robotic platforms target improved manipulation accuracy, stabilisation of instruments in the vasculature, and reduction of patient recovery times. However, benefits of recent platforms are undermined by a lack of haptics and residual patient exposure to ionising radiation. The purpose of this research was to design, implement, and evaluate a novel endovascular robotic platform, which accommodates emerging non-ionising magnetic resonance imaging (MRI). Methods: We proposed a pneumatically actuated MR-safe teleoperation platform to manipulate endovascular instrumentation remotely and to provide operators with haptic feedback for endovascular tasks. The platform task performance was evaluated in an ex vivo cannulation study with clinical experts (N = 7) under fluoroscopic guidance and haptic assistance on abdominal and thoracic phantoms. Results: The study demonstrated that the robotic dexterity involving pneumatic actuation concepts enabled successful remote cannulation of different vascular anatomies with success rates of 90% -100%. Compared to manual cannulation, slightly lower interaction forces between instrumentation and phantoms were measured for specific tasks. The maximum robotic interaction forces did not exceed 3 N. Conclusion: This research demonstrates a promising versatile robotic technology for remote manipulation of endovascular instrumentation in MR environments. Significance: The results pave the way for clinical translation with device deployment to endovascular interventions using non-ionising real-time 3D MR guidance.
An automatic laser focus adjustment on tissue surfaces based on stereoscopic scene information is feasible and has the potential to become an effective methodology for optimal ablation. Laser-to-camera registration facilitates advanced surgical planning for prospective user interfaces and augmented reality extensions.
Recent research has revealed that image-based methods can enhance accuracy and safety in laser microsurgery. In this study, non-rigid tracking using surgical stereo imaging and its application to laser ablation is discussed. A recently developed motion estimation framework based on piecewise affine deformation modeling is extended by a mesh refinement step and considering texture information. This compensates for tracking inaccuracies potentially caused by inconsistent feature matches or drift. To facilitate online application of the method, computational load is reduced by concurrent processing and affine-invariant fusion of tracking and refinement results. The residual latency-dependent tracking error is further minimized by Kalman filter-based upsampling, considering a motion model in disparity space. Accuracy is assessed in laparoscopic, beating heart, and laryngeal sequences with challenging conditions, such as partial occlusions and significant deformation. Performance is compared with that of state-of-the-art methods. In addition, the online capability of the method is evaluated by tracking two motion patterns performed by a high-precision parallel-kinematic platform. Related experiments are discussed for tissue substitute and porcine soft tissue in order to compare performances in an ideal scenario and in a setup mimicking clinical conditions. Regarding the soft tissue trial, the tracking error can be significantly reduced from 0.72 mm to below 0.05 mm with mesh refinement. To demonstrate online laser path adaptation during ablation, the non-rigid tracking framework is integrated into a setup consisting of a surgical Er:YAG laser, a three-axis scanning unit, and a low-noise stereo camera. Regardless of the error source, such as laser-to-camera registration, camera calibration, image-based tracking, and scanning latency, the ablation root mean square error is kept below 0.21 mm when the sample moves according to the aforementioned patterns. Final experiments regarding motion-compensated laser ablation of structurally deforming tissue highlight the potential of the method for vision-guided laser surgery.
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