Foreseeable challenges in developing telesurgery for low income and middle-income countries

Advancement in tele surgery or long distance telerobotic surgery is an intriguing prospect achieve equitable reach of global surgical services. In realization of this dream there are multiple of challenges as to telesurgery establishment and operation in low-and middle-income countries (LMIC). As of current market status, telesurgery is costly and not practical for the austere settings in these countries. “Telesurgery for a truly global surgery” is a realisable dream for the fore coming future. Affordable robotic surgical platforms, assistance from manufacturing companies to establish robotic platforms, introduction of 5G networking technology, international collaboration to unify efforts in telesurgery, and secure networking framework for a hassle free telesurgery network; are few formidable suggestions for implementing telesurgery in a global scale.

Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery

The capacity of machine objects to communicate autonomously is seen as the future of the Internet of Things (IoT), but machine-to-machine communication (M2M) is also gaining traction. In everyday life, security, transportation, industry, and healthcare all employ this paradigm. Smart devices have the ability to detect, handle, store, and analyze data, resulting in major network issues such as security and reliability. There are numerous vulnerabilities linked with IoT devices, according to security experts. Prior to performing any activities, it is necessary to identify and classify the device. Device identification and classification in M2M for secure telerobotic surgery are presented in this study. Telerobotics is an important aspect of the telemedicine industry. The major purpose is to provide remote medical care, which eliminates the requirement for both doctors and patients to be in the same location. This paper aims to propose a security and energy-efficient protocol for telerobotic surgeries, which is the primary concern at present. For secure telerobotic surgery, the author presents an Efficient Device type Detection and Classification (EDDC) protocol for device identification and classification in M2M communication. The periodic trust score is calculated using three factors from each sensor node. It demonstrates that the EDDC protocol is more effective and secure in detecting and categorizing rogue devices.

PRECISION IMPROVEMENT AND DELAY REDUCTION IN SURGICAL TELEROBOTICS

The field of medicine is greatly benefited by telerobotic surgeries. It allows the surgeon to reach the patient remotely. Traditional surgical telerobotic techniques had several drawbacks mainly due to communication delay and lack of precision. Signal latency also causes significant delay in operations. This also causes the risk of increased human errors. The surgical workflow can be altered by the addition of features like self-correction and self-automation. In this paper, we reduce the delay in telerobotic surgery using supervisory control approach and apply a haptic feedback to regulate safety. Motion scaling technique is used to combat responses in delayed environment as well as to improve the accuracy of the system. Augmented reality, improved functional design, comfort and skills along with the ability to reach the patient remotely promotes the wide use of surgical telerobotics.

Surgeon Training in Telerobotic Surgery via a Hardware-in-the-Loop Simulator

This work presents a software and hardware framework for a telerobotic surgery safety and motor skill training simulator. The aims are at providing trainees a comprehensive simulator for acquiring essential skills to perform telerobotic surgery. Existing commercial robotic surgery simulators lack features for safety training and optimal motion planning, which are critical factors in ensuring patient safety and efficiency in operation. In this work, we propose a hardware-in-the-loop simulator directly introducing these two features. The proposed simulator is built upon the Raven-II™ open source surgical robot, integrated with a physics engine and a safety hazard injection engine. Also, a Fast Marching Tree-based motion planning algorithm is used to help trainee learn the optimal instrument motion patterns. The main contributions of this work are (1) reproducing safety hazards events, related to da Vinci™ system, reported to the FDA MAUDE database, with a novel haptic feedback strategy to provide feedback to the operator when the underlying dynamics differ from the real robot’s states so that the operator will be aware and can mitigate the negative impact of the safety-critical events, and (2) using motion planner to generate semioptimal path in an interactive robotic surgery training environment.