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The Omega Medical Imaging, LLC Nyquist.IQ Image Processor is intended for use in Radiographic/fluoroscopic applications including cardiac, general radiographic/fluoroscopic diagnostic, and interventional x-ray imaging. The Nyquist.IQ is intended solely to be integrated only with Omega Medical Imaging CS-series-FP Systems.

Nyquist.IQ is a dynamic digital image processing system. The system application is based on a PC Windows operating system functioning on a PC based CPU. The object-oriented software performs real-time image processing and full procedure storage. The DICOM compliant connectivity provides the tools to transmit patient demographics, examination, and image data digitally.

Nyquist.IQ is not a standalone device, but functions as a component for FDA cleared Omega CS-series-FP platform. Nyquist IQ is an image processor that interfaces, with to acquire and digitize x-ray exposure from the Omega medical CS-series-FP.

The Nyquist.IQ operates in connection with the Varex's 3030 or the Teledyne 3030 flat panel detectors. This is demonstrated in the substantial equivalence section of the Nyquist.IQ is intended for the Omega CS-series-FP with Optional Accessory Device CA-100S / FluoroShield platform.

The Nyquist.IQ is intended solely to be integrated only with Omega Medical Imaging CS-series-FP systems.

The Nyquist.IQ image processor. The fundamental performance characteristics of the Nyquist.IQ interventional fluoroscopic imaging Processor system consists of:

• ) Real-time image visualization of patient anatomy during procedures

• Imaging techniques and tools to assist interventional procedures.

• 2 Post-processing functions after interventional procedures.
• 2 Storage of reference/control images for patient records.
• Σ Compatibility to images of other modalities via DICOM
• 2 Compatibility with the already FDA cleared CA-100S / FluoroShield AI Exposure Reduction Technology. (K182834)

This array of functions provides the physician the imaging information required to achieve minimally invasive interventional procedures.

The Nyquist.IQ image processor is available as a Model IPS-100 configuration and is similar to the currently marketed and predicate image processor MX-200 in CS-series-FP with optional CA-100S / FluoroShield Device.

This document is an FDA 510(k) clearance letter for the Omega Medical Imaging, LLC Nyquist.IQ Image Processor. It is a declaration of substantial equivalence to a predicate device, not a detailed study report with acceptance criteria and performance metrics for an AI/ML-driven device.

Therefore, this document does not contain the specific information required to answer your request regarding acceptance criteria and a study proving a device meets those criteria for an AI/ML product.

The information provided in the input primarily focuses on regulatory classification, indications for use, technological characteristics, and a comparison to predicate devices for a traditional medical imaging processor. It explicitly states:

• "The Nyquist.IQ did not require clinical study data since substantial equivalence to the currently marketed predicate device Omega CS-series-FP with Optional Accessory Device CA-100S / FluoroShield was demonstrated with the following attributes:
  • ∑ Indication for use.
  • A Technological characteristics.
  • ) Non-clinical performance testing; and
  • ∑ Safety and effectiveness." (Page 6)
• It also mentions a "FluoroShield AI Exposure Reduction Technology" associated with a predicate device (K182834), but it does not describe a performance study for the Nyquist.IQ as an AI/ML device itself. The Nyquist.IQ appears to be an image processor that interfaces with this existing technology, rather than being the AI/ML device itself.

To answer your specific questions, I would need a document that details a performance study, including acceptance criteria, for a software component with AI/ML functionality. This document does not provide such details.

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The Omega Medical Imaging, LLC CS-series-FP (SSXI) Systems are intended for use in Radiographic/fluroscopic applications including cardiac, vascular, general radiographic/fluroscopic diagnostic, and Interventional x-ray imaging.

The Omega Medical Imaging, LLC, CS-series-FP systems currently incorporate a 19.8cm x 19.8cm or 29.8cm x 29.8cm solid-state flat-panel detector (FPD). This 510(k) submission adds a slightly larger 21.7cm x 21.7cm and 30.3cm x 30.3cm solid-state CMOS flat-panel detector as an additional option. The CS-series-FP fluoroscopy single and dual plane x-ray imaging systems are configured with a floor mounted c-arm and patient table. The dual plane systems incorporate a ceiling suspended C-arm into the system. The MX CFP 3131 flat-panel image detector utilizes a cesium iodide scintillator coupled to an amorphous silicon TFT panel. The captured digital image is processed by the acquisition system (separate from the Flat Panel Detector) which includes image processing, viewing functions, local storage, and DICOM compatibility. The Image Processor does not have the capability to connect to the internet as there is no browser.

Subject Device Flat Panel Detectors MX CFP 2222 / 3131 are to be used only with the Omega CSseries-FP Fluoroscopic Systems

The document provided is a 510(k) premarket notification for a medical imaging device, specifically an X-ray system. It focuses on demonstrating substantial equivalence to a previously cleared predicate device, rather than proving the device meets specific clinical performance acceptance criteria against a ground truth in the way a diagnostic algorithm would.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts for ground truth, MRMC study, training set information) are not applicable to this type of submission.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

For this type of device (an X-ray system, specifically an optional detector component), the "acceptance criteria" are related to technical performance benchmarks and compliance with safety standards, rather than clinical diagnostic accuracy. The reported "performance" is the technical testing done to show equivalence.

• 21 CFR 1020.30, 21 CFR 1020.31, and 21 CFR 1020.32 (Parts related to electronic product radiation control).
• IEC 60601-1-2, IEC 60601-2-7, IEC 60601-2-28, IEC 60601-2-32, and IEC 60601-2-43 (International safety standards).
• UL 60601-1 and CAN/USA C22.2 No.601.1-M90 (Specific safety standards). |
  | Equivalence in Technology | The MX CFP 2222 and 3131 option utilizes the same technology (Cesium Iodide scintillator coupled to a CMOS-light sensitive imaging component) as the predicate device. |
  | No new indications for use | The modified device does not introduce any new indications for use. |
  | No new potential hazards | The modified device does not result in any new potential hazards. |

2. Sample size used for the test set and the data provenance:

• Sample Size: Not applicable. The "test set" in this context refers to commercially available test objects designed to evaluate technical imaging characteristics, not a clinical dataset of patient images.
• Data Provenance: Not applicable, as it's technical bench testing, not clinical data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. Ground truth for technical performance tests is established by the specifications of the test objects themselves and physical measurements, not by expert consensus on clinical images.

4. Adjudication method for the test set:

• Not applicable. Technical measurements do not typically involve adjudication in the way clinical diagnostic performance studies do.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No. This is an X-ray system (hardware component), not an AI algorithm. Therefore, an MRMC study and AI-related effectiveness are not relevant.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not applicable. This is an X-ray system (hardware component), not a standalone algorithm.

7. The type of ground truth used:

• Technical performance benchmarks established by the design of commercially available test objects (e.g., phantoms for low-contrast, spatial resolution, temporal resolution, dynamic range) and compliance with international and national safety standards.

8. The sample size for the training set:

• Not applicable. This is an X-ray system; there is no "training set" in the context of machine learning.

9. How the ground truth for the training set was established:

• Not applicable.

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