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| Name | Class |
|---|---|
| Politecnico di Milano | OTHER |
| Scuola Superiore di Studi Universitari e di Perfezionamento Sant'Anna | OTHER |
| KU Leuven | OTHER |
| SwissVortex |
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Cardiovascular diseases represent the most common cause of death worldwide. Percutaneous approaches with intravascular catheters are pivotal, since they allow to treat patients with high perioperative risks. However, catheter-based treatments require steep learning curves and are characterized by poor ergonomics and exposure to damaging radiation. ARTERY will offer a radiation-free approach based on shared-autonomy robotic catheters, with increased user engagement and easy interaction. Intraprocedural three-dimensional echocardiography as well as computed tomography images obtained during usual clinical practice will provide artificial intelligence algorithms that will turn catheter navigation to a simple task. Optical and electromagnetic sensing techniques will ensure a superior view upon the cardiovascular anatomy and will guide the autonomous catheter upon the interventionist supervision, who will be able to take over control at any instant.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Percutaneous cardiac procedures | Patients undergoing percutaneous cardiac procedures |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Percutaneous cardiac procedure | Procedure | Collection and analysis of intraprocedural information to provide artificial intelligence algorithms |
|
| Measure | Description | Time Frame |
|---|---|---|
| Identification of key procedural elements that will benefit from the technological developments introduced by ARTERY by collecting intraprocedural images | Number of patients undergoing percutaneous cardiac procedures | Intraoperative period |
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Inclusion Criteria:
Exclusion Criteria:
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Candidates to percutaneous cardiac defects repair
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Università Vita-Salute San Raffaele | Milan | MI | 20132 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39797996 | Background | Munafo R, Saitta S, Tondi D, Ingallina G, Denti P, Maisano F, Agricola E, Votta E. Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach. Med Biol Eng Comput. 2025 Jan 11. doi: 10.1007/s11517-024-03275-w. Online ahead of print. |
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| ID | Term |
|---|---|
| D006349 | Heart Valve Diseases |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| UNKNOWN |
| Artiness SRL | INDUSTRY |
| FBGS | UNKNOWN |
| IRCCS Ospedale San Raffaele | OTHER |
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