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Vista OS is an accessory to 1.5T and 3.0T whole-body magnetic resonance diagnostic devices (MRDD). It is intended to operate alongside, and in parallel with, the existing MR console to acquire traditional, real-time and accelerated images.

Vista OS software controls the MR scanner to acquire, reconstruct and display static and dynamic transverse, coronal, sagittal, and oblique cross-sectional images that display the internal structures and/or functions of the entire body. The images produced reflect the spatial distribution of nuclei exhibiting magnetic resonance. The magnetic resonance properties that determine image appearance are proton density, spin-lattice relaxation time (T1), spin-spin relaxation time (T2) and flow. When interpreted by a trained physician, these images provide information that may assist in the determination of a diagnosis.

Vista OS is intended for use as an accessory to the following MRI systems:

Manufacturers: GE Healthcare (GEHC), Siemens Healthineers
Field Strength: 1.5T and 3.0T
GE Software Versions: 12, 15, 16, 23, 24, 25, 26, 30
Siemens Software Versions: N4/VE; NX/VA

The Vista AI "Vista OS" product provides a seamless user experience for performing MRI studies on GE and Siemens scanners. The underlying software platform that we use to accomplish this task is called "RTHawk".

RTHawk is a software platform designed from the ground up to provide efficient MRI data acquisition, data transfer, image reconstruction, and interactive scan control and display of static and dynamic MR imaging data. It can control MR pulse sequences provided by Vista AI and, on scanners that support it, it can equally control MR pulse sequences provided by the scanner vendor. Scan protocols can be created by the user that mix and match among all available sequences.

RTHawk is an accessory to clinical 1.5T and 3.0T MR systems, operating alongside, and in parallel with, the MR scanner console with no permanent physical modifications to the MRI system required.

The software runs on a stand-alone Linux-based computer workstation with color monitor, keyboard and mouse. It is designed to operate alongside, and in parallel with, the existing MR console with no hardware modifications required to be made to the MR system or console. This workstation (the "Vista Workstation") is sourced by the Customer in conformance with specifications provided by Vista AI, and is verified prior to installation.

A private Ethernet network connects the Vista Workstation to the MR scanner computer. When not in use, the Vista Workstation may be detached from the MR scanner with no detrimental, residual impact upon MR scanner function, operation, or throughput.

RTHawk is an easy-to-use, yet fully functional, MR Operating System environment. RTHawk has been designed to provide a platform for the efficient acquisition, control, reconstruction, display, and storage of high-quality static and dynamic MRI images and data.

Data is continuously acquired and displayed. By user interaction or data feedback, fundamental scan parameters can be modified. Real-time and high-resolution image acquisition methods are used throughout RTHawk for scan plane localization, for tracking of patient motion, for detection of transient events, for on-the-fly, sub-second latency adjustment of image acquisition parameters (e.g., scan plane, flip angle, field-of-view, etc.) and for image visualization.

RTHawk implements the conventional MRI concept of anatomy- and indication-specific Protocols (e.g., ischemia evaluation, valvular evaluation, routine brain, etc.). Protocols are pre-set by Vista AI, but new protocols can be created and modified by the end user.

RTHawk Apps (Applications) are composed of a pulse sequence, predefined fixed and adjustable parameters, reconstruction pipeline(s), and a tailored graphical user interface containing image visualization and scan control tools. RTHawk Apps may provide real-time interactive scanning, conventional (traditional) batch-mode scanning, accelerated scanning, or calibration functions, in which data acquired may be used to tune or optimize other Apps.

When vendor-supplied pulse sequences are used in Vista OS, parameters and scan planes are prescribed in the Vista interface and images reconstructed by the scanner appear on the Vista Workstation. RTHawk Apps and vendor-supplied sequences can be mixed within a single protocol with a unified user experience for both.

Here's a breakdown of the acceptance criteria and study information for Vista OS, Vista AI Scan, and RTHawk, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes several clinical verification studies for new AI-powered features. Each feature has specific acceptance criteria.

2. Sample Sizes and Data Provenance for Test Sets

The document provides sample sizes for each clinical verification study test set:

• Automatic Detection of Motion Artifacts: 120 sample images.
• Automatic Detection of Ungateable Cardiac Waveforms: 100 sample ECGs.
• Automatic Cardiac Image Denoising: 209 sample image series (paired with non-denoised).
• Automatic Brain Localizer Prescriptions: 323 sample image localizations.
• Automatic Prostate Localizer Prescriptions: 329 sample image localizations.
• Automatic Prediction of Velocity-Encoding VENC: 42 sample VENC peak estimates.

Data Provenance:

• Data was "collected from prior versions of Vista OS."
• "Data used in clinical verification were obtained from multiple clinical sites representing diverse ethnic groups, genders, and ages."
• The document implies the data is retrospective as it was "collected from prior versions of Vista OS" and used for verification after model training.
• Specific countries of origin are not mentioned, but the mention of "multiple clinical sites" and "diverse ethnic groups" suggests a broad geographic scope.

3. Number of Experts and Qualifications for Ground Truth - Test Set

The document states:

• "Clinical assessments were performed by independent board-certified radiologists or cardiologists."
• The number of experts is not explicitly stated (e.g., "three experts"), but it says "cardiologist reader" for cardiac studies and "trained physician" for other interpretations, implying at least one expert per study type.
• Qualifications: "board-certified radiologists or cardiologists." Specific experience (e.g., "10 years of experience") is not provided.

4. Adjudication Method for Test Set

The adjudication method is not explicitly stated. It refers to "agreement between neural-network assessment... and the cardiologist reader" for cardiac studies, and "judged superior" for denoising, which suggests a single expert's assessment was used as ground truth for comparison. It does not mention methods like 2+1 or 3+1 consensus.

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

No MRMC comparative effectiveness study is explicitly mentioned. The studies focus on the performance of the AI models against expert assessment, not on comparing human readers with AI assistance versus without. The statement "all automations are provided as an additional aid to the trained operator who has the final decision power to accept or reject any suggestion or image enhancement that is provided" implies human-in-the-loop, but a specific MRMC study to quantify improvement is not described.

6. Standalone Performance Study

Yes, standalone (algorithm only without human-in-the-loop) performance studies were done for each of the new AI features. The acceptance criteria and reported performance directly measure the accuracy and agreement of the AI algorithm outputs against expert-established ground truth. The technologist retains the ability to reject or modify, but the initial validation is on the AI's standalone output.

7. Type of Ground Truth Used

The ground truth used for the clinical verification test sets was expert consensus / expert opinion.

• For artifact detection and ungateable waveforms: "cardiologist reader" assessment.
• For denoising: "diagnostic quality... judged superior" by "independent board-certified radiologists or cardiologists."
• For localizer prescriptions and VENC prediction: Implicitly, metrics like angular error, positional error, IoU, and velocity error are measured against a "correct" or "optimal" ground truth typically established by expert manual prescription or known physical values.

8. Sample Size for the Training Set

The document explicitly states that the test data was "segregated from training and tuning data." However, the exact sample size for the training set is not provided in the given text.

9. How Ground Truth for the Training Set Was Established

The document states:

• "Neural-network models were developed and trained using industry-standard methods for partitioning and isolating training, tuning, and internal testing datasets."
• "Model development data was partitioned by unique anonymous patient identifiers to prevent overlap across training, internal testing, and clinical verification datasets."
• "Clinical assessments were performed by independent board-certified radiologists or cardiologists who were not involved in any aspect of model development (including providing labels for training, tuning or internal testing)."

This implies that ground truth for the training set was established by expert labeling or consensus, but these experts were different from those who performed the final clinical verification. The exact number of experts involved in training data labeling and their qualifications are not specified.

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