Coronary vessel detection methods for organ‐mounted robots

Rasmussen, Eric; Shiao, Eric Chiwei; Zourelias, Lee; Halbreiner, M. Scott; Passineau, Michael J.; Murali, Srinivas; Riviere, Cameron N.

doi:10.1002/rcs.2297

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Targets were located within 2 mm using the single equivalent moving dipole mapping (SEMDM) technique. Feasibility of ablation via needle insertion was assess in an artificial tissue model and ex vivo tissue and within 2 mm of the target depth 46 . Further studies showed that doppler ultrasound signals were successfully used to identify and avoid coronary vascular during cardiac interventions using the robotic device 47 .…”

Section: Future Epicardial Access and Mapping Techniques: Preclinical...mentioning

confidence: 99%

“…Feasibility of ablation via needle insertion was assess in an artificial tissue model and ex vivo tissue and within 2 mm of the target depth. 46 Further studies showed that doppler ultrasound signals were successfully used to identify and avoid coronary vascular during cardiac interventions using the robotic device. 47 Robotic assisted percutaneous pericardial access has not yet been shown to be feasible.…”

Section: Robotic-assisted Epicardial Mapping and Ablationmentioning

confidence: 99%

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Current and novel percutaneous epicardial access techniques for electrophysiological interventions: A comparison of procedural success and safety

Tonko,

Lambiase

2023

Cardiovasc electrophysiol

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Accessing the pericardial space safely and efficiently is an important skill for interventional cardiac electrophysiologist. With the increased recognition of the complexity of the 3‐dimensional arrhythmogenic substrate due to advances in imaging and mapping technologies there has been an expansion of epicardial procedures in recent years. Equally, minimally invasive implantation of epicardial pacing, cardiac resynchronization, or defibrillation leads is expanding in specific patients where transvenous systems are contraindicated or their long term sequelae should be ideally avoided. Selective delivery of intrapericardial pharmacological antiarrhythmic therapy is yet another potential indication, albeit still investigational. The expanding indications for percutaneous epicardial procedures is contrasted by the still substantial risk and challenges associated with accessing the pericardial space. Myocardial perforation, coronary artery laceration, and damage to the surrounding organs are all recognized and feared complications. A number of innovative epicardial access techniques have been proposed to overcome the difficulties and risks of traditional dry subxiphoid punctures and may allow for more widespread use of epicardial access in the future. We review 10 different established and novel subxiphoidal epicardial access techniques describing procedural success rates, safety profile and overall experience. The technical aspects as well as access times and costs for extra equipment will be reviewed. Finally, an outlook of reported preclinical techniques awaiting in‐human feasibility studies is provided.

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Section: Future Epicardial Access and Mapping Techniques: Preclinical...mentioning

confidence: 99%

Section: Robotic-assisted Epicardial Mapping and Ablationmentioning

confidence: 99%

Current and novel percutaneous epicardial access techniques for electrophysiological interventions: A comparison of procedural success and safety

Tonko,

Lambiase

2023

Cardiovasc electrophysiol

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Artificial Intelligence and Its Application in Cardiovascular Disease Management

Namasivayam

Senguttuvan

Saravanan

et al. 2022

Machine Learning and Systems Biology in Genomics and Health

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Blood Vessels Disease Detection of Coronary Angiography Images using Deep learning Model

Osama,

Kumar,

Shahid

2024

Preprint

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Presently Coronary artery disease, often caused by the narrowing of the coronary artery lumen due to atherosclerosis, is a leading cause of death. Coronary angiography also known as cardiac catheterization or X-ray angiography, is a medical procedure that uses X-ray imaging to visualize the coronary arteries, which supply blood to the heart muscle. X-ray angiography is procedure to assess the blood flow through these arteries and to identify any blockages or abnormalities. The accuracy of X-ray angiography depends on the quality of the imaging equipment as well as experience and expertise of the radiologist. Poor image quality could affect the accurate diagnosis of coronary arteries. Manual interpretation of angiography images is subjective and time consuming. In some cases, small or diffuse blockages may not be easily visible, and additional imaging techniques may be required. Therefore, early automated detection of blockage of heart vessels became necessary for detection and diagnosis. The artificial intelligence algorithms could play a vital role in this area. In this paper, a deep-learning based algorithm has been used for recognition of blockage in coronary angiographic visuals. Here, we proposed deep learning (YOLOv8) models for the detection of blockage into blood vessels coronary angiography images. In this experiment about 1934 labelled X-ray angiography images has been used from Mendeley. For Experimentation purpose, images are preprocessed and augmented. Total 80% images have been used for training and 20% images has been used for testing. The experimental results show that the measuring metrices of proposed model for detection of blood vessels blockage area in rectangular box. The performance of model represented by predicted value of Precision, recall, mean average precision (mAP) and F1 score are, 99.4%, 100%, 99.5% and 99.7% respectively.

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Coronary vessel detection methods for organ‐mounted robots

Cited by 3 publications

References 46 publications

Current and novel percutaneous epicardial access techniques for electrophysiological interventions: A comparison of procedural success and safety

Current and novel percutaneous epicardial access techniques for electrophysiological interventions: A comparison of procedural success and safety

Artificial Intelligence and Its Application in Cardiovascular Disease Management

Blood Vessels Disease Detection of Coronary Angiography Images using Deep learning Model

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