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The OEC® Brivo® Mobile C-Arm X-Ray Products are designed to provide digital spot-film imaging and fluoroscopic image quidance for all adult and pediatric populations for orientations between patient anatomy and surgical instruments. The product is used for general surgical applications and musculoskeletal procedures to visualize, for example, implant localization/s or needle positions for aspirations, injections or biopsy. The OEC® Brivo® is not indicated for interventional use.

The OEC® Brivo® is a system used to assist trained physicians. The system is used to provide X-Ray images while the physician performs a medical procedure. Images from the system help the physician to visualize the patients' anatomy. This visualization helps to localize surgical regions of interest and pathology. The images provide real-time visualization and records of pre-surgical anatomy, in vivo-surgical activity and post-surgical outcomes. The system is composed of two primary physical elements. The first is referred to as the "C-Arm" because of its "C" shaped image gantry; the second referred to as the "Workstation" because this is the primary user interface to the system. The system employs X-Rays as its imaging technology. An X-Ray Generator located in the base of the C-Arm creates high voltage. High voltage is carried to the X-Ray tube across a set of cables. The X-Ray tube emits X-Rays that are directed toward the patient under the control of the operator. The Physician defines the desired view for the specific clinical procedure and directs the operator. The X-Rays pass through the patient and are captured by the image intensifier (II). Image intensifier images are captured by a camera and displayed on the image monitor located on the Workstation. The system operator and/or Physician view the images as they are displayed and they may choose to store the images for later review.

This document describes the 510(k) Premarket Notification Submission for the GE Healthcare OEC® Brivo® Mobile Fluoroscopic Imaging System. This is a medical imaging device, not an AI/ML algorithm, so many of the requested criteria for AI/ML studies are not applicable.

Here's an analysis based on the provided text, focusing on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a fluoroscopic imaging system and not an AI algorithm performing a specific task like classification or detection, the "acceptance criteria" are related to compliance with medical device standards and the generation of clinically useful images. Device performance is demonstrated through various non-clinical tests.

2. Sample Size Used for the Test Set and the Data Provenance

• Test Set Sample Size:
  • External Image Quality Evaluation: Implied to be images from "human cadavers." No specific number is provided.
  • Product Simulated Testing Performance Use: Uses "anthropomorphic phantoms." No specific number of phantoms or images is provided.
  • Functional Product Simulated Use Testing: Uses "simulated clinical workflow" with no specific numbers of cases or procedures given.
• Data Provenance:
  • The "External Image Quality Evaluation" used human cadavers, implying pre-collected or specifically prepared cadaveric data.
  • The "Product Simulated Testing Performance Use" used anthropomorphic phantoms.
  • The document does not mention specific countries of origin for the cadaver or phantom data.
  • The testing described is prospective in nature, as it involves active testing of the device.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

• Number of Experts:
  • Functional Product Simulated Use Testing: "an orthopedic surgeon and trained clinical technicians." This suggests at least one orthopedic surgeon and an unspecified number of technicians.
  • External Image Quality Evaluation: "orthopedic surgeons and clinical representatives." This suggests at least two types of evaluators, with an unspecified number for each.
• Qualifications of Experts:
  • "orthopedic surgeon"
  • "trained clinical technicians"
  • "clinical representatives"
  • No specific years of experience or board certifications are provided in the document.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1 consensus). The evaluations appear to be direct assessments by the stated experts and technicians. For "External Image Quality Evaluation," where multiple types of evaluators are mentioned, it states "Evaluation of the images by orthopedic surgeons and clinical representatives demonstrated OEC® Brivo® produces useful images," which might imply a collaborative or individual assessment without a formalized adjudication process for discrepancies.

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 MRMC study was done. This device is a fluoroscopic imaging system, not an AI-powered diagnostic or assistive tool for image interpretation by clinicians. The study aims to demonstrate the device's ability to produce useful images and comply with standards, not to measure reader performance improvement with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• N/A. This is a hardware imaging system, not an algorithm. Its performance is its standalone performance in generating images. The evaluations of image quality and functional use are assessments of the system's output.

7. The Type of Ground Truth Used

• For image quality and clinical utility:
  • Expert assessment/consensus: Orthopedic surgeons and clinical representatives evaluated the "usefulness" of images produced from cadavers and phantoms.
  • Functional verification: The "Product Simulated Use Testing" confirmed the system conforms to user needs and intended uses by an orthopedic surgeon and technicians.
  • Compliance with standards: Verification against established regulatory and international standards (e.g., 21 CFR, IEC 60601 series).

8. The Sample Size for the Training Set

• N/A. This document describes a traditional medical device (fluoroscopic imager), not an AI/ML algorithm that requires a training set. The "design inputs" and "user needs" for its functions would have guided its development, rather than a data-driven training process.

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

• N/A. As no training set is applicable for this type of device, no ground truth for a training set was established.

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