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The Cara System is intended for preplanning and guidance of medical interventions in an area known to contain or be adjacent to the cardiac conduction system, such as percutaneous or surgical procedures, for example, transcatheter aortic valve replacement (TAVR), as well as medical procedures where the physician desires to deliver therapy to the patient's cardiac conduction system or to a targeted location within it (CSP).

The Cara System uses computed tomography angiography (CTA)-based and user manually marked landmarks to identify the cardiac conduction axis and generate a three-dimensional (3D) map of the individual patient's cardiac conduction system. The system also overlays the anatomical location of the cardiac conduction system (generated by the Cara Metis Simulator using pre-procedure CT data) onto live fluoroscopic images.

The software utilizes AI/ML algorithms to provide OCR detection, automated segmentation of anatomical structures, and detection of catheters.

The CARA System is intended for use in adult patients (18 years of age and older).

The CARA System is a medical device comprising two integrated functions. The CARA System device components include the CARA Metis Simulator and the CARA Atlas Navigator. Both components provide diagnostic imaging software and hardware functions that identify the personalized anatomical location of the cardiac conduction system in relation to other heart anatomies based on a patient's computed tomographic angiography (CTA). The former is intended for preplanning (1) a medical intervention in an area known to contain or be adjacent to the cardiac conduction system or (2) a medical procedure(s) where the physician desires to deliver therapy to the patient's cardiac conduction system. The latter identifies the personalized anatomical location of the cardiac conduction system overlaid on real-time, intra-procedural, fluoroscopic imaging and provides guidance during interventional structural heart disease procedures in an area known to contain or be adjacent to the cardiac conduction system or where the physician desires to deliver therapy to the patient's cardiac conduction system.

The CARA Metis Simulator uses computed tomography angiography (CTA)-based landmarks to accurately identify the cardiac conduction axis and run a simulation generating the personalized three-dimensional (3D) map of the individual patient's cardiac conduction system.

This 3D map is then utilized by the clinical operator to plan any procedure to either target, as in direct pacing, or avoid as in structural heart disease interventions, the cardiac conduction system. As described below, this technology is based on methodical translational studies investigating the 3D location of the cardiac conduction system relative to cardiac structures visible by clinical imaging with initial assessment and validation in the clinical setting.

The CARA Atlas Navigator is designed to overlay the personalized anatomical location of the cardiac conduction system (generated by the Cara Metis Simulator using pre-procedure CT data) onto live fluoroscopic images. This functionality assists clinicians during fluoroscopy-guided interventional heart procedures.

The Cara Atlas Navigator consists of both software and hardware components:

1. Fluoroscopy Splitter (F-Splitter) – This device splits the live fluoroscopy image for integration with the CARA System.

2. CARA Box – A standard workstation that receives live fluoroscopy images from the Fluoroscopy Splitter and enhances them by adding anatomical landmarks. The CARA Box acts as the system's central processing unit, handling data analysis and image processing. It is equipped with user interface devices, such as a mouse and keyboard.

3. CARA Monitor – Displays the enhanced fluoroscopy images, including the analysis performed by the CARA Box. This monitor is typically located in the control room. The same output is also projected onto the main display in the operating room.

The CARA System utilizes a specific on-premises workflow to ensure data integrity and clinical accuracy. Prior to physician use, a certified CARA Clinical Expert (CCE) must be physically present on-site. The CCE logs into the CARA Box workstation to prepare the CARA Metis pre-planning process. This includes initiating the automated segmentation, verifying the anatomical output, annotating landmarks, and saving the results to the local storage.

The physician subsequently logs into the same workstation using distinct credentials to load, review, and confirm the pre-planned case. This workflow ensures that all generated outputs are professionally prepared and verified before clinical review.

The CARA System utilizes AI/ML algorithms to provide OCR (Optical Character Recognition), automated segmentations and device tracking:

• OCR detection - is used to automatically extract metadata from the live feed of the fluoroscopy machine (e.g., c-arm position, focal distance, etc.).
• Segmentations - the system utilizes deep learning models to automatically generate anatomical segmentations of the heart chambers and aorta.
• Device Detection - using a segmentation model the system detects the distal tips of specific interventional devices (e.g., Pigtail catheters, CS catheters, pacing leads) within the fluoroscopic image to support real-time tracking and present overlay.

AI-based segmentations are provided to assist the workflow but may contain inaccuracies. The AI output should not be used as the sole basis for clinical decision-making. Clinical oversight is mandatory.

Here's a breakdown of the acceptance criteria and study details for the CARA System, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance Study for the CARA System

The CARA System's performance was evaluated through non-clinical, AI/ML validation, and retrospective clinical performance testing to demonstrate substantial equivalence to the predicate device, Cydar EV (K212442).

1. Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for AI/ML Test Set

• OCR Test Set: 61 fluoroscopic images (retrospective, multi-site clinical datasets).
• Anatomical Segmentation (Cardiac Chambers) Test Set: 50 retrospective CT scans (retrospective, multi-site clinical datasets).
• Aortic Segmentation Test Set: 480 fluoroscopic images (retrospective, multi-site clinical datasets).
• Catheter & Lead Detection Test Set: 2,139 fluoroscopic images (retrospective, multi-site clinical datasets).

The specific country of origin for the retrospective, multi-site clinical datasets is not detailed in the provided information.

3. Number and Qualifications of Experts for Ground Truth

• Anatomical Segmentation (Cardiac Chambers) Ground Truth: Manual segmentation by trained technologists, adjudicated by a U.S. Board-Certified Interventional Cardiologist.
• Aortic Segmentation Ground Truth: Manual contour annotation, adjudicated by a U.S. Board-Certified Interventional Cardiologist.
• Catheter & Lead Detection Ground Truth: Manual distal tip annotation, adjudicated by a U.S. Board-Certified Interventional Cardiologist.
• OCR Ground Truth: Manual verification of extracted parameters (no specific expert qualifications mentioned beyond "manual verification").

The number of experts (U.S. Board-Certified Interventional Cardiologists) used for adjudication is not specified (e.g., whether it was one individual or a panel).

4. Adjudication Method for the Test Set

The adjudication method clearly states "adjudicated by a U.S. Board-Certified Interventional Cardiologist." This implies a single expert review of the preliminary ground truth established by trained technologists/manual annotators. It does not indicate a 2+1 or 3+1 consensus method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was mentioned in the provided document. The study described focuses on the device's standalone performance and a retrospective clinical correlation, not on comparing human reader performance with and without AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done for the AI/ML algorithms. The "AI/ML Performance Summary" table directly details the performance of the OCR, anatomical segmentation, and catheter/lead detection algorithms on independent test datasets, measured against ground truth.

7. Type of Ground Truth Used

• AI/ML Algorithms: Expert consensus (adjudication by a U.S. Board-Certified Interventional Cardiologist) applied to initial manual annotations by trained technologists for anatomical segmentations and catheter/lead detection. Manual verification for OCR.
• Clinical Performance Data: Retrospective clinical outcomes data (Permanent Pacemaker Implantation rates, Left Ventricular Ejection Fraction improvement) associated with the CARA-visualized Conduction System Axis and Left Bundle Branch Pacing.

8. Sample Size for the Training Set

The document states, "Algorithms were trained using retrospective, multi-site clinical datasets," but does not specify the sample size used for the training set. It only mentions that "Training and test datasets were independent."

9. How the Ground Truth for the Training Set was Established

The document states, "Algorithms were trained using retrospective, multi-site clinical datasets." While it describes how ground truth was established for the validation/test set (manual segmentation by trained technologists with physician adjudication), it does not explicitly detail how the ground truth for the training set was established. It is implied that similar methods would have been used, but it's not directly stated.

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