Medical imaging analysis via artificial intelligence algorithms
Medical imaging analysis via artificial intelligence algorithms: Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes
Study summary
This non-interventional study aims to use artificial intelligence to improve the prediction of transcatheter heart valve interventions and optimize patient outcomes. It is based on the analysis of retrospective data from various specialized centers worldwide.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
Inclusion Criteria:
Patients who have reached the age of legal majority under local laws.
* For TAVI group: All patients who have had TAVI with a third generation transcatheter heart valve (THV), with an available pre-procedural optimal quality CT scan as defined by an ECG- gating CT with:
1. five to ten image volumes at cardiac phases from 5% to 95% R-R
2. 0.625 mm slice thickness
3. 0.625 mm spacing between slices
4. 0.88 mm in-plane pixel spacing
* For TMVI group: Patients who have had a TMVI with a dedicated device and screen failures, with an available optimal quality CT scan.
* For TTVI group: Patients who have had a TTVI with a dedicated device and screen failures, with an available optimal quality CT scan.
* For M-TEER: All patient who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Mitral valve, frame per second equal or higher than 40 frames per second, acceptable 3D reconstructions.
* For T-TEER: All patient who have had a T-TEER with G4 or newer iteration of TriClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Tricuspid valve, frame per second equal or higher than 40 frames per second, acceptable transgastric image with acceptable 3D reconstructions.
Exclusion Criteria:
* For TAVI group: Valve-in-valve procedures
* For TMVI group: Valve-in-valve and valve-in-ring procedures
* For TTVI: Valve-in-valve and valve-in-ring procedures
* For M-TEER: G3 or older MitraClip, G1 Pascal
* For T-TEER: G3 Triclip, G1 Pascal
Primary outcome measure(s)
Accuracy of transcatheter AI predictions — Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes. Validation of artificial intelligence algorithms for automatic segmentation of anatomic structures and imaging measurements, and prediction of the success of transcatheter interventions.
Output of AI algorithm:
* Sizes, types, and number of devices to be implanted
* Device success
* Percentage risk of permanent pacemaker implantation (for TAVI and TTVI)
* Percentage risk of 30-day (para)valvular regurgitation for TAVI, and residual regurgitation for M-TEER and T-TEER
* Single leaflet detachment for M-TEER and T-TEER
* Left ventricular outflow tract obstruction for TMVI.
Key success indicators:
* First, independent retrospective validation dataset AI algorithms predict procedural outcome with \>90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.
* Second independent retrospective dataset, perform a study to validate AI algorithms with \>90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.
Trial sites (15)
Facility
City
Region
Status
Montefiore Medical Center New York
New York
New York
Recruiting
Montreal Heart Institute, 5000 Rue Bélanger, Montréal
Montreal
Quebec
Recruiting
St Michael's Hospital Toronto
Toronto
Canada
Recruiting
St Paul's Hospital Vancouver
Vancouver
Canada
Recruiting
Centre Hospitalier Universitaire (CHU) de Bordeaux, 12 rue Dubernat 33404 Talence cedex
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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