The role of robotic technology in cardiac surgery

Quint, Elise; Sivakumar, G.

doi:10.5206/uwomj.v87i2.1254

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Cardiac surgery and other specialty areas have seen little RAS penetration and some safety concerns [3,1]. In cardiac surgery, for example, the limited access to the heart, no space available for the slave instrument maneuvering and close proximity to other vital structures require finer slave instrument movements and a superior teleoperation to that available in current robotic systems.…”

Section: Introductionmentioning

confidence: 99%

Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation

Sani¹,

Ascione²,

Dogramadzi³

2021

Preprint

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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 and fine motion. These limitations make tele-operated robotic surgery not suitable for micro-surgery and 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 microsurgery 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 15 finger/thumb/wrist joint angle sensors to generate meaningful data-sets 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 sur-

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Section: Introductionmentioning

confidence: 99%

Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation

Sani¹,

Ascione²,

Dogramadzi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Among many available robotic systems, Da Vinci is currently the only widely used in hospitals [3]. In the field of cardiac surgery, however, RAS has not been widely adopted [3] and its application is mainly limited to mitral valve repair [2][3][4] and in smaller extent, coronary artery revascularization [2,3], closure of simple septal defects, tricuspid valve repair, and cardiac tumor removal [4] . In the literature, cost and steep learning curve of currently available systems are counted as reasons for this [1,3,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…In the field of cardiac surgery, however, RAS has not been widely adopted [3] and its application is mainly limited to mitral valve repair [2][3][4] and in smaller extent, coronary artery revascularization [2,3], closure of simple septal defects, tricuspid valve repair, and cardiac tumor removal [4] . In the literature, cost and steep learning curve of currently available systems are counted as reasons for this [1,3,5,6]. Despite Da Vinci system with 7 DOF master station and a wide range of tools the control of fine movements required in heart surgeries is still an issue [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Towards Finger Motion Tracking and Analyses for Cardiac Surgery

et al. 2019

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Robot Assisted Surgery is attracting increasing amount of attention as it offers numerous benefits to patients as well as surgeons. Heart surgery requires a high level of precision and dexterity, in contrast to other surgical specialties. Robot assisted heart surgery is not as widely performed due to numerous reasons including a lack of appropriate and intuitive surgical interfaces to control minimally invasive surgical tools. In this paper, finger motion of the surgeon is analyzed during cardiac surgery tasks on an ex-vivo animal model with the purpose of designing a more intuitive master console. First, a custom finger tracking system is developed using IMU sensors, which is lightweight and comfortable enough to allow free movement of the surgeons fingers/hands while using instruments. The proposed system tracks finger joint angles and fingertip positions for three involved fingers (thumb, index, middle). Accuracy of the IMU sensors has been evaluated using an optical tracking system (Polaris, NDI). Finger motion of the cardiac surgeon while using a Castroviejo instrument is studied in suturing and knotting scenarios. The results show that PIP and MCP joints have larger Range Of Motion (ROM), and faster rate of change compared to other finger/thumb joints, while thumb has the largest Fingertip WorkSpace (FWS) of all three digits.

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

Sani

Ascione

Dogramadzi

2021

J. Med. Robot. Res.

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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.

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The role of robotic technology in cardiac surgery

Cited by 3 publications

References 9 publications

Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation

Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation

Towards Finger Motion Tracking and Analyses for Cardiac Surgery

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

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