Deep learning robotic guidance for autonomous vascular access

Chen, Alvin I.; Balter, Max L.; Maguire, Timothy J.; Yarmush, Martin L.

doi:10.1038/s42256-020-0148-7

Cited by 121 publications

(83 citation statements)

References 65 publications

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“…In many proof of principle studies on the surgical autonomy, various phantoms, not living animal or human patient are employed, such as porcine small intestine [6] , porcine skin [17] , and in vitro tissue-mimicking models [37] . Likewise, in the current study, we use the simulation bleeding scenario on the pieces of ex vivo porcine tissue to demonstrate the feasibility of the task autonomy of the blood removal operation.…”

Section: Discussionmentioning

confidence: 99%

Autonomous Robot for Removing Superficial Traumatic Blood

Shi

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective : To remove blood from an incision and find the incision spot is a key task during surgery, or else over discharge of blood will endanger a patient’s life. However, the repetitive manual blood removal involves plenty of workload contributing fatigue of surgeons. Thus, it is valuable to design a robotic system which can automatically remove blood on the incision surface. Methods : In this paper, we design a robotic system to fulfill the surgical task of the blood removal. The system consists of a pair of dual cameras, a 6-DoF robotic arm, an aspirator whose handle is fixed to a robotic arm, and a pump connected to the aspirator. Further, a path-planning algorithm is designed to generate a path, which the aspirator tip should follow to remove blood. Results : In a group of simulating bleeding experiments on ex vivo porcine tissue, the contour of the blood region is detected, and the reconstructed spatial coordinates of the detected blood contour is obtained afterward. The BRR robot cleans thoroughly the blood running out the incision. Conclusions : This study contributes the first result on designing an autonomous blood removal medical robot. The skill of the surgical blood removal operation, which is manually operated by surgeons nowadays, is alternatively grasped by the proposed BRR medical robot.

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Section: Discussionmentioning

confidence: 99%

Autonomous Robot for Removing Superficial Traumatic Blood

Shi

et al. 2021

IEEE J. Transl. Eng. Health Med.

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“…This results in more than US$0.5-1 billion of annual healthcare cost 31 . Portable robotic devices are able to use deep learning for autonomous blood vessel access despite anatomic variability, and can provide superior success rates in shorter time compared to human-employed manual access 32 . Especially in times of high demand, these technologies can mitigate contamination risk to healthcare workers and reduce economic burden.…”

Section: The Rise Of Robots In Surgical Environments During Covid-19mentioning

confidence: 99%

The rise of robots in surgical environments during COVID-19

2020

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“…Chen et al [10] describe a portable robotic device capable of introducing needles and catheters into deformable tissues such as blood vessels to draw blood or deliver fluids autonomously. These Robotic cannulation predictions are driven by a series of deep convolutional neural networks that encode spatiotemporal information from multimodal image sequences to guide real-time serving.…”

Section: Related Workmentioning

confidence: 99%

The Role of Object Detection, Tracking and Augmentation in Guiding the Remote Assistance Systems

2021

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Establishing a remote assistance system which will guide the user in repair or maintenance works of any manufacturer machine is quite challenging. The present paper proposes a solution where the user will train a deep learning models to detect different devices and devices parts. The paper discusses different solutions used in training the models and compares these models in detecting the objects of devices with respect to accuracy and time. The tracking and real-time detection of the objects help the user to perform any remote task with the help of an expert. The solution is ported in all types of mobile devices where users can see the devices and devices parts with augmented information on top of that. This solution will help the user to take remote tasks with the help of expert where expert cannot reach the field for repair or maintenance of devices. The entire solution is deployed for dialysis machine use case for performing multiple repair or maintenance activities of the dialysis machine. The accuracy of these models and model performance in real-time also play a key role as part of our remote assistance tasks.

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Deep learning robotic guidance for autonomous vascular access

Abstract: edical robots promise enhanced precision, safety and efficacy by working beyond the limits of human perception and dexterity 1,2. Recent advancements in image guid

Cited by 121 publications

References 65 publications

Autonomous Robot for Removing Superficial Traumatic Blood

Autonomous Robot for Removing Superficial Traumatic Blood

The rise of robots in surgical environments during COVID-19

The Role of Object Detection, Tracking and Augmentation in Guiding the Remote Assistance Systems

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