Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion During Conventional Microsurgery Using Machine Learning

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human–robot interfaces lack intuitive teleoperation and cannot mimic surgeon’s hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon’s gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon’s feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument’s pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon’s hand/finger movements with a mean squared error (MSE) less than 0.3%. Conclusion: We have developed a system to capture fine movements of the surgeon’s hand during micro-surgery that could enhance future remote teleoperation of similar surgical tools during micro-surgery. More work is needed to refine this approach and confirm its potential role in teleoperation.

Kombinasi Dexmedetomidine – Sevoflurane 0,5 MAC pada Bedah Mikro Reseksi Malformasi Arteri-Vena

Arterio-venous malformation (AVM) is a rare case, particularly among young patients (<40 years old). Maintaining haemodynamic stability and anticipating massive haemorrhage during micro surgery resection of AVM are fundamental for an anaesthetist. Total Intra Venous Anesthesia using propofol is still popular to control intracranial pressure as it is easily titrated and fast acting agent (both in onset and duration). Moreover, general neuruologic evaluation soon after anesthesia terminated is an integral important component of microsurgery of brain MAV. In this case report: a 20-year-old woman suddenly lost her consciousness and left-sided motors strength. Brain angiographic revealed an AVM in right frontal lobe. Microsurgery of brain AVM resection was performed. After 5-minute-preoxygenation, anaesthetic induction was performed by using propofol, fentanyl, rocuronium, and sevoflurane. The surgery went successfully using a combination of dexmedetomidine-sevoflurane 0.5MAC. Post-anaesthesia hemodynamic of this patient was in stable and without new neurologic deficit afterward.

An information centric framework for creating virtual environments to support micro surgery

In this paper we discuss the creation of an information centric framework to develop a virtual reality environment for micro surgery. An information model of micro surgery was built through interactions with an expert micro surgeon using the engineering Enterprise Modeling Language (eEML). The overall approach and architecture of the micro surgical environment is discussed. An enterprise level surgical manager, surgical planner and other components work together to enable the functioning of the virtual environment for micro surgery. Such virtual environments are essential to educating / training young budding surgeons..