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The product is designed to provide fluoroscopic and digital spot images of adult and pediatric patient populations during diagnostic, interventional, and surgical procedures. Examples of a clinical application may include: orthopedic, gastrointestinal, endoscopic, urologic, neurologic, vascular, critical care, and emergency procedures.

The OEC One CFD is a mobile C-arm X-ray system to provide fluoroscopic images of the patient during diagnostic, interventional, and surgical procedures such as orthopedic, gastrointestinal, endoscopic, urologic, neurologic, vascular, critical care, and emergency procedures. These images help the physician visualize the patient's anatomy and localize clinical regions of interest. The system consists of a mobile stand with an articulating arm attached to it to support an image display monitor (widescreen monitor) and a TechView tablet, and a "C" shaped apparatus that has a flat panel detector on the top of the C-arm and the X-ray Source assembly at the opposite end.

The OEC One CFD is capable of performing linear motions (vertical, horizontal) and rotational motions (orbital, lateral, wig-wag) that allows the user to position the X-ray image chain at various angles and distances with respect to the patient anatomy to be imaged. The C- arm is mechanically balanced allowing for ease of movement and capable of being "locked" in place using a manually activated lock.

The subject device is labelled as OEC One CFD.

Here's a summary of the acceptance criteria and the study details for the OEC One CFD device, specifically focusing on the new "Deep learning-based Trajectory Pointer software" feature, as that's where the most detailed study information is provided in the document.

1. Table of Acceptance Criteria and Reported Device Performance (for Trajectory Pointer)

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

• Total image dataset: 3078 images
• Test set size: 307 images (approx. 10% of the total dataset)
• Data Provenance (Ethnicity/Country):
  • US: 51.1%
  • China: 35.8%
  • Phantom: 9.8%
  • EU/Australia: 3.3%
• Data type: The document states that sample clinical images were evaluated to demonstrate substantial equivalence, and "all images were collected from GE OEC Mobile C-arm devices," suggesting retrospective collection of clinical images, alongside phantom data. It also notes that the test data covers the "entire range of values for all confounders," implying it's representative of real-world scenarios.

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

• Number of Experts: 3
• Qualifications: U.S. board-certified radiologists. (No specific years of experience are listed).

4. Adjudication Method for the Test Set

• The document states: "3 U.S. board certified radiologists check annotations to make sure the ground truth is correct."
• This implies a consensus or verification process by multiple experts to validate the initial manual annotations, but a specific method like "2+1" or "3+1" is not explicitly mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate human reader improvement with AI assistance. The performance metrics reported are for the algorithm's standalone performance.

6. If a Standalone (Algorithm Only) Performance Study was Done

• Yes, a standalone performance study was done for the Trajectory Pointer feature. The reported success rates of 98.8% for LIH images and 97.1% for Fluoroscopy images (both exceeding the 95% MAAP) are measures of the algorithm's performance.

7. The Type of Ground Truth Used

• For the Trajectory Pointer feature, the ground truth was established by expert consensus/manual annotation. Specifically, "the contours of K-wires were manually outlined by trained annotators," and "3 U.S. board certified radiologists check annotations to make sure the ground truth is correct."

8. The Sample Size for the Training Set

• Training set size: 2771 images (3078 total images - 307 test images = 2771 images). The document states: "Total dataset included 3078 images, 307 were used for test dataset and the rest for training/tuning."

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

• While not explicitly detailed for the training set, it is highly probable that the ground truth for the training data was established in a similar manner to the test set: through manual outlining of K-wire contours by trained annotators. The document mentions "comparing ground truth annotations" during deduplication, implying annotations were available for the training data.

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The Digiscan FDX Family, a Mobile C-Arm X-Ray System, is intended to provide Fluoroscopic images of the patient during diagnostic, surgical procedures.

Clinical applications may include (but not limited to) orthopedic, Fertility studies (HSG), Gl procedures like endoscopy , neurology, urology, critical care and emergency room procedures.

Digiscan FDX C-Arm is indicated for visualization in real time and/or recording of surgical region of interest and anatomy, using X-Ray imaging technique.

Exclusion: Digiscan FDX Family is not recommended for Mammography.

Contraindications: Exposure of X-Ray should be avoided during pregnancy.

The Digiscan FDX Family (Digiscan FDX-V, Digiscan FDX-R and Digiscan FDX-S) are mobile X-Ray C-Arm fluoroscopic device used by radiation experts. Digiscan FDX family is a digital fluoroscopic imaging system with Solid State X-Ray Image Detetors (FPD) used in diagnostic. The device is designed in such a way that it can moved around and can be positioned for the required be anatomical/clinical/procedural position.

Digiscan FDX family composed of C-Arm, X-Ray generating equipment (X-Ray controller, high voltage generator, X-Ray tube), FPD, and workstation (Console computer and Monitor(s)). C-Arm unit with generator is capable of movements which are essential for patient positioning, like horizontal travel, orbital movement, vertical movement, wig-wag movement and C rotation. The X-Ray generator, X-Ray control system and collimator controls are housed in the C-Arm unit.

Synergy FDX-CR imaging software is a Digital Imaging System (DIS) designed for C-arm Fluoroscopic Mobile X-Ray System. Synergy FDX-CR imaging software provides useful functions to manage X-Ray images obtained from Digiscan FDX family FPD Fluoroscopic Mobile X-Ray System.

The provided text does not describe acceptance criteria for an AI/ML powered device, nor does it present a study proving the device meets particular acceptance criteria. The document is a 510(k) summary for the Allengers Medical Systems Limited's Digiscan FDX, a Mobile C-Arm X-Ray System. It focuses on demonstrating substantial equivalence to predicate and reference devices, rather than establishing direct performance against specific acceptance criteria for an AI component.

However, based on the provided text, I can infer some aspects of performance testing and equivalence argument:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative table format for performance. Instead, it compares detailed technical specifications of the "Digiscan FDX (Subject Device)" against a "Ziehm Vision RFD (Predicate Device)" and "OEC Elite" and "Cios Fusion" (Reference Devices). The "Discussion of Difference" column informally indicates whether the subject device's performance characteristics are considered "Same," "Similar," or have "No impact on safety or efficacy."

Here's a summary of the comparisons, which serve as the de-facto "performance evaluation" in this substantial equivalence submission:

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

• Test Set Sample Size: Not explicitly stated as a number of "cases" or "patients" in the context of a dataset for algorithmic evaluation.
• Data Provenance: Not applicable as this is a traditional X-ray system, not an AI/ML device that processes data. The "clinical tests" involved "independent views" on "acquired images" from procedures, but these are not described as a formal 'test set' for an AI algorithm.

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

• Number of Experts: "Independent views of Urologist, orthopedic, Gastroenterologists, Neurologist were obtained." The specific number of experts is not quantified (e.g., 2, 3, or more).
• Qualifications: "Urologist, orthopedic, Gastroenterologists, Neurologist." No details on their years of experience or board certification are provided. They are referred to as "radiation experts" within the device description.
• Ground Truth Establishment: Their views confirmed that "the acquired images were of adequate quality for the indicated use." This indicates a qualitative assessment of image quality, not a ground truth for a diagnostic outcome that an AI would predict.

4. Adjudication Method for the Test Set:

• Not applicable in the context of an AI/ML device with an adjudication process. The experts provided "independent views," suggesting no formal adjudication method (like 2+1 or 3+1 consensus) was explicitly described for discrepancy resolution.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size of Human Improvement with AI vs. without AI Assistance:

• No, an MRMC comparative effectiveness study involving AI assistance was not conducted or described. This submission is for a conventional X-ray system, not an AI-powered diagnostic tool. The document establishes substantial equivalence based on technical specifications and safety standards, and qualitative clinical assessment of image quality.

6. If a Standalone (Algorithm Only) Performance Study was done:

• No, a standalone (algorithm only) performance study was not done. The device is a fluoroscopic X-ray system, not an AI algorithm.

7. The Type of Ground Truth Used:

• The "ground truth" for the device's performance was the qualitative assessment of image adequacy by medical experts. The "acquired images were of adequate quality for the specific view and procedures identified in the IFU," as determined by Urologists, Orthopedists, Gastroenterologists, and Neurologists. This is not a pathology report, an outcome measure, or an expert consensus for a diagnostic AI task.

8. The Sample Size for the Training Set:

• Not applicable. This is not an AI/ML device, so there is no "training set."

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

• Not applicable. As there is no training set for an AI/ML component, no ground truth was established for it.

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